Currently Global Navigation Satellite Systems (GNSSs) are the primary source for the determination of absolute position, navigation, and time (PNT) for merchant vessel navigation. Nevertheless, the performance of GNSSs can strongly degrade due to space weather events, jamming, and spoofing. Especially the increasing availability and adoption of low cost jammers lead to the question of how a continuous provision of PNT data can be realized in the vicinity of these devices. In general, three possible solutions for that challenge can be seen: (i) a jamming-resistant GNSS receiver; (ii) the usage of a terrestrial backup system; or (iii) the integration of GNSS with other onboard navigation sensors such as a speed log, a gyrocompass, and inertial sensors . The present paper focuses on the third option by augmenting a classical IMU/GNSS sensor fusion scheme with a Doppler velocity log. Although the benefits of integrated IMU/GNSS navigation system have been already demonstrated for marine applications, a performance evaluation of such a multi-sensor system under real jamming conditions on a vessel seems to be still missing. The paper evaluates both loosely and tightly coupled fusion strategies implemented using an unscented Kalman filter (UKF). The performance of the proposed scheme is evaluated using the civilian maritime jamming testbed in the Baltic Sea. Maritime transport plays a key role in the global trade with nearly 80% of commodity volumes and 70% of commodity values being transported by sea . The incThis dependency on GNSS has raised serious concerns on the vulnerability of the navigation process ,4. As thThe International Maritime Organization (IMO) recognized the importance of resilient onboard provision of PNT data, and the development of Guidelines for an onboard PNT (data processing) unit has been identified as supplementary and necessary. The German Aerospace Center (DLR) has developed a concept for such a PNT unit  and was In general, one can distinguish three main groups of solutions that can be used for mitigation of GNSS jamming. Within the methods of the first group, the impact of jamming is mitigated inside the GNSS receiver by techniques such as adaptive notch filtering, pulse blanking, or adaptive beamforming using a multi-antenna GNSS receiver ,12,13. WThe present paper focuses on an example from the third group, where a classical inertial measurement unit (IMU)/GNSS sensor fusion scheme is augmented with a Doppler velocity log (DVL). Although the benefits of integrated IMU/GNSS navigation systems have already been demonstrated for marine applications , a perfoThe rest of the paper is organized as follows. In The emerging of safety-critical applications subject to accurate GNSS positioning and timing, such as autonomous cars or assisted landing, has urged the GNSS community into identifying jamming as one of the major menaces ,18,19. TA series of works addressing the impact of jamming attacks on the maritime navigation has been recently presented. For example, a trial was conducted on the East coast of United Kingdom using a professional L1 band jammer . This stSubsequently, a jamming experiment was carried out in the north of Norway using a broadband (bandwidth ≈ 60 MHz) jammer, centered at GPS L1 frequency and affecting the GLONASS primary frequencies . The maiOne of the typical approaches to bridge short GNSS outages is to combine GNSS information with that from an inertial measurement unit (IMU). The inertial sensors measure the relative state of the object with respect to the inertial navigation frame and have distinct advantages, such as being self-contained, immune to interference, and highly dynamical. Unfortunately, these systems provide only incremental information and the integration outputs drift over time when no external reference is provided . An earlAnother sensor typically used in maritime applications is the Doppler velocity log (DVL). When mounted on a vessel, the sensor faces the sea floor and can provide very accurate velocity information of the vessel with respect to the sea floor  for scenThe proposed hybrid navigation system employs the recursive Bayesian estimation (RBE) framework to combine the outputs of several sensors in order to obtain a single navigation solution. The RBE methods deal with the problem of estimating the changing-in-time state of the system applying a priori information about the underlying system dynamics and the observations from the sensors. The probabilistic paradigm has been widely applied to navigation and tracking problems, given its ability to accommodate inaccurate models as well as imperfect sensors .In general, any RBE cycle is performed in two steps:Prediction: The a priori probability is calculated from the last a posteriori probability using the available process model.Correction: The a posteriori probability is calculated from the a priori probability using the measurement model and the current measurements.Among the multiple RBE methods, the Kalman filter (KF) is probably the most well-known solution for the navigation problem. The original KF provides an optimal solution when the models are linear and the probabilities are Gaussian. However, when dealing with nonlinearities in the models, as is often the case for positioning problems, nonlinear extensions of KF such as the extended KF (EKF) or the unscented KF (UKF) can be used. In the case of the EKF, the nonlinear models are linearized around the most recent state estimate, while in the UKF the probability distribution is approximated using a set of deterministically chosen (non-randomly sampled) points in the state space, where every sample is assigned a particular fixed weight. This set of points conserves the Gaussian properties of the distribution under nonlinear transformations .de facto standard for tracking applications [Although the EKF has been for a long time considered as a ications , the UKFications . The UKFications .n, m, In general, the navigation filter is formulated as a nonlinear estimation problem for the system governed by the following stochastic models:q represents the attitude quaternion from the vessel local to the the earth-centered, earth-fixed (ECEF) frames, v and p are the 3D velocity and position in the ECEF frame, and In our work, the vector Effective and accurate attitude estimation can be considered as a key component of almost any higher performance navigation system. Within our implementation, we have explicitly addressed a challenge of attitude estimation by separating the attitude part (quaternion) and the vector part of the estimated kinematic state. In the present work, we chose a unit quaternion as the attitude parametrization due to its computational efficiency, low redundancy, and absence of singularity. The unit quaternion, although consisting of four numbers, is deprived of one degree of freedom due to its unit norm constraint and, therefore, requires a special treatment within the framework of the UKF . Except loosely coupled approach, a snapshot least-square adjustment is applied to estimate the position and velocity of the vessel. In the case of a classical code-based GNSS positioning, a time-of-arrival concept is employed to determine the receiver position from the measured code pseudoranges of the satellites in view. The positioning principle is based on solving a geometric problem from the measured ranges to the set of visible satellites with known coordinates [There are several options to construct the measurement models depending on the configuration of the filter. For a rdinates . The iontightly coupled integration, avoids the intermediate snapshot position and velocity solutions by applying the code or Doppler measurements directly in the measurement models of the filter. This strategy puts no inherent restrictions on the number of satellites available and even fewer than four available measurements could still be employed to constrain, at least partially, the possible drift of the estimated position. The same holds for the Doppler GNSS measurements, which can be combined with the GNSS code-ranges to enforce a radial velocity measurement over each available link. The mathematical model to relate the pseudorange j refers to the c stands for the speed of light, I gather the tropospheric and ionospheric effects, respectively, and Note that at least four satellites have to be visible in order to solve explicitly for both the position and the velocity in loosely coupled architecture. If there are fewer than four satellites visible at a given epoch, no solution can be found and the GNSS measurement step of the loosely coupled filter has to be skipped. An alternative approach, also known as nsulting .In the case of a loosely coupled integration, an equivalent position solution error covariance can be calculated by using the geometry matrix from the iterated LS solution and a corresponding weight matrix, where the weights are assigned following the assumptions regarding the quality of each link. A simplification, commonly used in practice  is that the measurements from the different satellites are uncorrelated. The measurement weighting matrix can then be constructed as an inverse of the diagonal measurement noise covariance matrix. As the GNSS Doppler measurements are usually of a relatively high quality, a constant noise assumption can be easily adopted with an equivalent circular covariance approximation for Doppler LS solution in a loosely coupled configuration.Finally, the DVL measurement model can be considered as the X–Y velocity measurement in the coordinate frame of the sensor including the lever arm compensation with respect to the IMU. In addition to the actual 2D velocity measurement, a constraint along the body vertical axis of the vessel can be employed (velocity projection in the body frame) as one can assume the vertical velocity to be zero on average. The constraint has been implemented within the UKF framework as so-called “pseudo-measurement” by extending the true sensor measurement with a third component, setting the measurement to zero with some associated measurement noise. This vertical velocity measurement significantly decreases the vertical position drift by reducing it from being cubic in time to becoming quasi-linear in time. The measurement model for the DVL speed observation Naturally, for lower-cost IMUs the navigation performance is strongly degraded due to a fast accumulation of the errors caused by sensor noises, biases, scale factor errors, etc. Moreover, for non-augmented IMU/GNSS system , the attitude and some of the inertial sensor errors become weakly observable, and the ability of the system to effectively estimate them is strongly conditioned on the dynamics of the vessel. For these reasons, the baseline observations (non-collinear vector measurements) were incorporated from three spatially distributed GNSS antennas to ensure that the attitude drift is constrained when baseline measurements are available. Recall that the attitude integration is the first integral of three subsequent integrals, which constitute the strapdown inertial mechanization and the attitude errors start to dominate the other error sources relatively fast. A baseline observation is considered to be valid if the RTK-based baseline determination between two antennas resulted in a fixed integer ambiguity solution; therefore, up to three baseline observations can be incorporated into the measurement model at each epoch depending on the quality of the RTK solutions. Here, baseline estimation is realized in a loosely coupled manner, following the methodology presented in . NonetheIn order to enable experimental jamming tests under real life maritime conditions, the DLR in cooperation with the German Federal Network Agency has allocated a civilian maritime GNSS jamming testbed in the Baltic Sea. The test area is located approximately 10 km North of the Darß Peninsula see . The meaThe applied PPD jammer was sweeping a continuous wave signal with an update rate of ∼ 10  MHz see  affectin++ as described in [The measurement equipment was installed on BALTIC TAUCHER II, the multipurpose research and diving vessel  calculation with the RTKLib software  in post-A and B constitutes a nonlinear regression problem, solved here by applying a least-squares adjustment. In In ined see :(7)σ=A×, as introduced in In In During the second jammer passing, the number of tracked satellites varies several times around four, with a maximum of 6 and a minimum of 0 satellites. Here, the tightly coupled filter still shows some advantages, although the loosely coupled filter performs reasonably well.The largest difference between the filters can be observed during the third passing of the jammer. During that time span with a GPS outage, for a short period of time, four satellites were tracked. The GDOP for this four satellite constellation is larger than 50  jammer was installed on a moored vessel. A second vessel, equipped with three separately placed GNSS antennas and receivers, an inertial measurement unit (IMU), and a Doppler velocity log (DVL) passed the jammer several times with varying distances. The influence of the jammer on the three antennas varied significantly, with the GPS single point positioning being partially disrupted up to a distance of approximately 3 km and corresponding positioning errors up to 200 m.A successful usage of GPS pseudorange measurements under challenging jamming conditions is only possible if the measurements still follow assumed test statistics within the sensor fusion algorithm. In order to determine the pseudorange error statistic under a jammed condition, a lab experiment was performed, where the output of a GNSS antenna was merged with the signal of an attenuated PPD jammer before being fed to a GNSS receiver. The analysis of this experiment shows that, as expected, the carrier-to-noise density (CN0) decreases with increasing jamming power, and the number of tracked satellites is therefore significantly reduced. Unexpected is the fact that the standard deviation of the pseudorange error is only slightly increased by the jammer. A CN0-based variance model, which is typically assumed to be the best model, substantially overestimates the error in the presence of a jammer. Out of this analysis, a standard elevation-based noise model or even a constant noise model appear to be good candidates for the application in the sensor fusion scheme.The evaluation of the proposed IMU/GNSS/DVL UKF within the challenging jamming scenario confirmed the superior performance of the tightly coupled approach when compared to the loosely coupled scheme. Here, the advantage of the tightly coupled UKF is mainly due to a direct usage of the measurements in the segments affected by the jamming, where the number of tracked satellite drops partially below four. Due to the usage of the DVL, the filter shows only a linear position drift during complete GPS outages. This can be seen as a substantial advantage when compared to classical GNSS/IMU integration, where the positioning error grows cubically in time due to triple integration of the gyroscope errors in a strapdown navigation system. The analysis of the different weighting schemes for the GPS pseudorange measurements inside the UKF yielded rather small differences between the approaches. The best performance was achieved using the elevation-based variance model and, therefore, confirms the results of the lab experiment. The maximum horizontal position error of less than 30 meters of the tightly coupled IMU/GNSS/DVL UKF for the challenging jamming environment is small enough to successfully support most of the maritime applications.Future work will deal with the reduction of the observed constant linear drift of 5 m/min by trying to align better the DVL and GNSS compass or, alternatively, considering a constant alignment error within the estimator. Additionally, the simultaneous usage of the measurements from all three antennas and the performance of the filter with available stable reference clock will be explored. With respect to the pseudorange error model in the vicinity of jammer, we will extend the lab experiment in order to calibrate an error model based on both the elevation and carrier-to-noise density ratio. Finally, an appropriate error model for the GNSS Doppler measurements needs to be developed.


Deep and slow abdominal breathing is an important skill for the management of stress and pain. However, despite multiple proofs on the effectiveness of biofeedback, most breathing apps remain limited to pacing specific breathing patterns, without sensor feedback on the actual breathing behavior.Breathing-Mentor was developed. This app combines effective visualization of the instruction with biofeedback on deep abdominal breathing, based on the mobile phone’s accelerometers. The aim of this pilot study was to investigate users’ feedback and breathing behavior during initial contact with the app.To fill this gap, an app named To reveal the possible effects of biofeedback, two versions of the mobile app were developed. Both contained the same visual instruction, but only the full version included additional biofeedback. In total, 40 untrained participants were randomly assigned to one of the two versions of the app. They had to follow the app’s instructions as closely as possible for 5 min.F1,37=4.18; P<.048). During this initial contact with the full version, self-reported relaxation effectivity was, however, lower than the group using visual instruction without biofeedback , probably owing to increased cognitive workload to follow the instruction.The group with additional biofeedback showed an increased signal-to-noise ratio for instructed breathing frequency (0.1 Hz) compared with those using visual instruction without biofeedback (This study supports the feasibility and usefulness of incorporating biofeedback in the Breathing-Mentor app to train abdominal breathing. Immediate effects on relaxation levels should, however, not be expected for untrained users. Chronic stress has been identified as a critical factor that influences people’s physical and mental well-being ,2. HowevDeep and slow diaphragmatic breathing can lead to a state of relaxation. Therefore, it is frequently taught as a basic strategy for the management of stress, anxiety, posttraumatic stress disorder , and paiBesides pacing, providing biofeedback is another approach for breathing trainings eg, -24). Wit. Wit24])In this study, biofeedback refers to feedback about the movement of the abdomen during a breathing task. One example for this kind of biofeedback is the BellyBio Interactive Breathing app for iOS devices by RelaxLine. It uses the mobile phone’s built-in accelerometers to capture the abdominal breathing movements. For deep and slow breathing, the sound of the ocean is transformed to relaxing music. However, so far, no study has analyzed the effectiveness of such abdominal breathing feedback. Moreover, the app is recommended only for people who are already familiar with breathing exercises. Direct instructions should be used for novices instead .Breathing-Mentor app is a biofeedback breathing app that was developed to provide such direct instructions. It combines the effective wave-base visualization of the desired breathing pattern [The  pattern  with bioBreathing-Mentor. The focus of this study was to determine the users’ ability to follow the breathing instructions and their subjective usage experience.To investigate the feasibility and usefulness of the additional biofeedback, a control version without biofeedback, that is, with visual instruction only, was implemented as well. For this purpose, we conducted a user study to reveal how people who are unfamiliar with breathing exercises deal with both versions of The biofeedback signal is drawn over the sine wave . It is obtained from the mobile phone’s accelerometers, given that the mobile phone is correctly positioned on the user. The latter is supported through an interactive calibration procedure. During the study, the mobile phone was fixed in a custom  frame, and the latter was fixed with an elastic band around the upper abdomen. The overall signal processing approach for transforming the accelerometer measurements into the breathing signal as visualized on the screen and used for data analysis is detailed below.Accelerometers measure 3D linear acceleration, a combination of body acceleration and acceleration resulting from gravity, in the local sensor coordinate system. As the participants are stationary during breathing training, the acceleration resulting from gravity constitutes the major portion of the measurement. Moreover, in the training target pose, this component provides information about lateral and anterior and posterior tilt of the mobile phone with regard to the sagittal and transversal body plane, respectively see . The basThese assumptions were confirmed in pretests  with 5 persons already trained in abdominal breathing. For these trials, the frame including the mobile phone was positioned by the investigator  with its base above the center of the upper abdomen, so that the mobile phone’s long edge was approximately leveled, and the display was facing the person. The recorded accelerometer data from these trials were then used to obtain the major dispersion direction as the first principal component. A reference range  was extracted by projecting all recorded accelerometer vectors onto this principal component and calculating the average minimum and maximum values over the test persons.The average accelerometer vector was also used as reference vector for aiding a repeatable positioning of the custom frame on the participants of the study and thus improving the validity and reliability of the extracted breathing signal. For this, the app provided an interactive calibration procedure with traffic light feedback on the angle deviations between the currently measured accelerometer vector and the reference vector in the xy-plane and in the xz-plane  gave their informed consent for inclusion in the beginning. They were randomly assigned to the experimental group (EG) with biofeedback or the control group (CG) without biofeedback. In the beginning, previous experience with breathing exercises for relaxation was screened as described by Chittaro and Sioni ; see theThe 3 measurement blocks are described in t37=−0.93, P=.36) or sex ratio .A total of 40 participants took part in the pilot study. One person from the CG was excluded owing to a chronic respiratory disease, resulting in a final sample size of 39. The mean age was 26.51 years . Groups did not differ significantly with regard to age  was compared between both groups in a variance analysis with repeated measurements to check for systematic differences between the groups. Neither systematic group effects nor interaction of group with frequency bands was expected for the baseline block.For the training block, the following 2 measures of the objective breathing behavior comparable with the study of Chittaro and Sioni  were calThe first measure, the spectral power in the recommended frequency band (0.09-0.11 Hz), indicates how intensely the respiratory act is performed for the recommended frequency band. The second measure, respiratory signal-to-noise ratio (SNR), describes the ratio between the power of the recommended breathing frequency band (0.09-0.11 Hz) and the power in the entire breathing spectrum  allows investigating changes in performance over time. Both groups are expected to increase performance over time for both dependent measures.Both, the spectral power in the recommended frequency band as well as the respiratory SNR are expected to increase in both groups for the training block owing to the visual instruction for the 0.1-Hz breathing rate, compared with the baseline condition. If the additional biofeedback actually enhances performance during the breathing exercise, there should be a main effect of group for both dependent measures. The additional within-subject factor To test whether a single 5-min training session is already enough to cause changes in the abdominal breathing patterns toward the requested breathing frequency (0.1 Hz), the spectral power in the recommended frequency and the SNR of the postmeasurement block were compared in both groups with the baseline in 2 variance analyses with repeated measures.t tests for independent samples (group comparisons) and t tests for dependent samples (comparisons between blocks and minutes) are described. Please note that the given sample size only allows to reveal large effect sizes (0.8). F and P values are described in the context of variance analyses and t and P values for t tests.For single comparisons, The screening for previous experience with breathing exercises for relaxation revealed no systematic differences between groups .For the baseline block, there was a main effect of frequency bands with more power for frequency bands near the normal breathing rate  as well as in the SNR  in the training block compared with the baseline block . There was a main effect of group but no interaction between group and time , we compared the baseline and the postmeasurement block with regard to the spectral power of this frequency in both groups. There were no main effect of measurement block, no effect of group, and no interaction between block and group  were more prominent in both groups compared with slower frequencies. This shows that participants were rather novices for slow abdominal breathing exercises. This finding agrees with the results from the questionnaire on previous experience with breathing exercises for relaxation purposes. Although most participants were aware that abdominal breathing can be used for relaxation exercises, only few participants actually reported practicing such exercises. Thus, the participants were representative of users who could benefit from a training app for diaphragmatic breathing .Indeed, both versions of Breathing-Mentor  enabled the users to realize the requested breathing frequency of 0.1 Hz more accurately compared with the baseline, as reflected by the spectral power and the SNR. This was expected, as both conditions include the wave-based visual instruction, which has already been shown to be very effective for mobile breathing training . MoreoveThe main research question of this study was, how additional biofeedback in a mobile app, as implemented in Breathing-Mentor (see the Methods section for details), influences people’s ability to follow the visual breathing instruction and their subjective usage experience.Although the spectral power of the desired frequency band did not result in significant group differences, the SNR was higher for the biofeedback training group (see the Results section for details). This means that abdominal breathing at the desired frequency was not more prominent compared with the CG without biofeedback, but the occurrence of undesired frequency bands was reduced for the biofeedback group, resulting in enhanced SNR values. These findings support the effectiveness of the additional biofeedback on breathing behavior.This benefit in performance was, however, combined with lower subjective ratings regarding the effectiveness of the app to reduce stress and ease with which app instructions could be followed for the biofeedback training. This result could be a consequence of increased cognitive workload and attention resources that are required to interpret and modulate the biofeedback graph . NeverthTo summarize, Breathing-Mentor seems to be a useful tool to teach specific abdominal breathing patterns. An immediate improvement of the user’s relaxation state should, however, not be expected, especially for persons who are inexperienced with breathing tasks. With further experience, tools such as the BellyBio Interactive Breathing app might be more useful, as the auditory feedback allows to close the eyes and to focus more intensively on the body, which are facilitating factors for deep relaxation . Such auFinally, the frame that is used to hold the mobile phone at a stable position is 1 limitation factor. Although there were no user complaints concerning the usability of this approach, the correct positioning of the mobile phone was guaranteed by the calibrating procedure and the principal investigator in this study. Other fixing solutions should be considered for everyday use.In summary, it should be noted that participants were rapidly able to adjust their breathing pattern to the instruction (within 1 min). This result supports the feasibility and usefulness of biofeedback in mobile breathing apps based on the mobile phone’s accelerometers, especially for people who are unfamiliar with breathing techniques. Immediate effects on the user’s relaxation state should, however, not be expected.


A trusted routing scheme is very important to ensure the routing security and efficiency of wireless sensor networks (WSNs). There are a lot of studies on improving the trustworthiness between routing nodes, using cryptographic systems, trust management, or centralized routing decisions, etc. However, most of the routing schemes are difficult to achieve in actual situations as it is difficult to dynamically identify the untrusted behaviors of routing nodes. Meanwhile, there is still no effective way to prevent malicious node attacks. In view of these problems, this paper proposes a trusted routing scheme using blockchain and reinforcement learning to improve the routing security and efficiency for WSNs. The feasible routing scheme is given for obtaining routing information of routing nodes on the blockchain, which makes the routing information traceable and impossible to tamper with. The reinforcement learning model is used to help routing nodes dynamically select more trusted and efficient routing links. From the experimental results, we can find that even in the routing environment with 50% malicious nodes, our routing scheme still has a good delay performance compared with other routing algorithms. The performance indicators such as energy consumption and throughput also show that our scheme is feasible and effective. Wireless sensor network (WSN) is a promising technology to collect and send information to the clients through the self-organization network in the way of a single-hop or multi-hop relay, which has a wide application prospect in military national defense, environmental science, industry, agricultural automation and other fields ,2,3,4,5.In time-varying and dynamical WSN environments, existing routing schemes cannot accurately distinguish the malicious nodes. In some specific routing algorithms, the routing nodes cannot distinguish the truth of routing information released by other routing nodes. As shown in (i)The first is the distributed ledger which contains all the transactions on the blockchain. The contents of these transactions include the address of the receiver of the transaction, the amount of the transfer, the timestamp, the smart contract code, the execution result of the smart contract, etc. The transaction ledger is completed jointly by multiple nodes in different places. Each node in the blockchain keeps a complete ledger, so that no ledger information can be tampered. While all of the nodes can participate in monitoring the legality of transactions.(ii)The second is the asymmetric encryption and authorization technology. The transaction information stored on the blockchain is public, but the account identity information is highly encrypted and can only be accessed under the authorization of the data owner, thus ensuring the security of data and personal privacy.(iii)PoW: Bitcoin, Dogecoin and Litecoin are among the digital currencies based on the PoW consensus mechanism. PoW algorithm relies on the node to carry out mathematical operations to find a random number and obtain the accounting right. A malicious node needs more than 51 percent of the network’s computing power to take control of the blockchain network. Compared with other consensus mechanisms, the resource consumption of the PoW blockchain is high and the supervision is weak. At the same time, every time a PoW consensus is reached, the whole network needs to participate in the operation, which has low performance and efficiency.PoS: The main idea of the PoS consensus mechanism is that the difficulty of obtaining the accounting right of a node is inversely proportional to the stake held by the node. According to the proportion and time of coins taken by each node, the difficulty of mining coins can be reduced in the same proportion so as to speed up the speed of finding random numbers. The greater the stake, the greater the privilege, the greater the responsibility to generate the block and the power to generate more revenue.PoA: PoA is an improved algorithm of PoS that uses the verified identity of the nodes to replace the role of the stake rather than the monetary value. In a PoA blockchain, the transaction and the block are validated by an approved node  without a huge computational overhead of a mining process. The validator must authenticate on the blockchain and the qualification is hard to acquire which means the validator will not have a motive for acting on their own interests. Even if there is a malicious validator, it will be kicked out by other validators’ votes. In this way, the PoA blockchain becomes safer and cheaper.The third part, called consensus mechanism, is how all accounting nodes reach consensus to determine the effectiveness of a blockchain transaction, which is a means of preventing tampering. Some common consensus algorithms including proof of work (PoW), proof of stake (PoS), proof of authority (PoA), delegated proof of stake (DPoS) and proof of capacity (PoC) are discussed in ,28. We i(iv)The last technical element is the smart contract, which is based on the trusted and non-tampering data and can automatically execute the predefined codes by a blockchain miner . The exeIn view of the above security issues, a third-party intermediary is proposed to solve the trust problem between the routing nodes, but the intermediary is obviously not suitable for multi-hop distributed wireless sensor networks. Meanwhile, a third-party trust management center is likely to be attacked and controlled by malicious nodes, and therefore the security and fairness of the system cannot be guaranteed. As a trusted, decentralized, self-organizing ledger system, the blockchain is very suitable for multi-hop distributed wireless sensor networks ,21,22,23In an open, reliable and distributed blockchain network, a routing node can acquire routing information including but not limited to its neighbor routing nodes. The efficiency of the routing can be improved if this routing information is properly used. Some routing schemes have introduced the reinforcement learning into the dynamically networks ,32,33,34In this work, we introduce a novel trusted routing scheme based on blockchain and reinforcement learning for WSNs. In particular, we use the blockchain technology to provide a distributed routing information management platform that all the routing information is recorded on the blockchain through the blockchain token transactions. The scheme takes advantage of the decentralized, tamper-proof and traceable characteristics of the blockchain transactions to improve the trustworthiness of the routing information between the routing nodes. We exploit the reinforcement learning to learn the dynamic, reliable and extensive routing information from the blockchain network. A dynamically updated reinforcement learning model is generated in each routing node through the dynamically updated reward value brought by the action (scheduling) of each state (packet location), so as to help the routing nodes make better routing decisions and select the more reliable and efficient routing links.In this part, we firstly review the traditional trusted routing schemes for improving the routing security and reliability. Then, we introduce the relevant research approaches of routing schemes using the blockchain technology. Finally, we investigate the state-of-the-art of the application of the reinforcement learning in routing networks.Providing a trusted routing environment is an important and difficult issue for WSNs. There are many related researches to implement a trusted routing scheme. Li et al. designed a novel trust-based routing protocol by extending the widely used AODV (ad hoc on-demand distance vector) routing protocol . The proRecently, some people combined the tamper-proof and traceable characteristics of the blockchain technology with routing algorithms to improve the trustworthiness between the routing nodes. Gómez-Arevalillo et al. presented a trusted public key management framework named secure blockchain trust management (SBTM) . The appIt is difficult to effectively utilize the dynamic routing information in WSN routing networks. A self-adaptive routing algorithm is needed for such dynamic routing networks. To enhance the self-adaptability of routing scheme, Boyan et al. were the first time to combine Q-learning algorithm with packet routing to dynamically learn the routing situation to find the shortest path . ReinforIn this section, we introduce a novel trusted routing scheme based on the blockchain and reinforcement learning. First, we put forward the threat model of our scheme and briefly describe the attack and cheating methods of malicious nodes in the routing environment. Then, we propose a blockchain-based network architecture to strengthen the credibility of routing information. We also design a specific routing scheduling algorithm based on reinforcement learning for the designed blockchain-based network architecture, labeled as RLBC (reinforcement learning and blockchain based) routing algorithm, in which the reinforcement learning is used to help the routing node select the next optimal routing node. Finally, we analyze the security of the proposed trusted routing scheme.In this paper, we assume that the blockchain network is trusted, that is, no attacker can control the blockchain network by controlling more than half of the server nodes. We further assume that the routing nodes are untrusted and the vulnerable routing nodes may be controlled by malicious attackers. In a routing scheduling process, a malicious routing node can falsely claim to have sent a certain number of packets to a routing node or deny receiving packets sent by other routing nodes. The malicious routing nodes can release false routing information on the routing network, such as queue length information, thus affecting the routing scheduling process. They can also act as black hole attack nodes and refuse to forward packets. However, we do not consider the collusion attack by two routing nodes to complete invalid blockchain transactions. We further assume that a routing node can only act as a normal or malicious node, which means attacks are by no means intermittent. Meanwhile, we do not consider the occasional abnormal behavior caused by the performance of the node .S, routing node R, and terminal device.To enhance the trustworthiness and robustness of the routing information, we introduce the blockchain which is essentially a distributed ledger with tamper-proof, decentralization, and information traceability characteristics into the wireless sensor network and use the blockchain token transactions to record related information of each node, as shown in R has its own LAN to connect several terminals and is responsible for receiving packets from other routing nodes or terminals, as well as forwarding the received packets to the target nodes. We briefly describe the routing flow; packets from the source terminal to the destination terminal are transmitted to a routing node In Validator: The validators are the pre-authenticated nodes of the blockchain, which have advanced authorization and are responsible for the verification work in the PoA blockchain. In our system, each server node is a validator with higher rights in the PoA blockchain and has a unique blockchain address. Their specific tasks include executing smart contracts, verifying blockchain transactions, and releasing blocks on the blockchain. A new validator can be added by the authenticated validators election with more than 50% of the votes. Even if there is a malicious validator, it can only attack one of the contiguous blocks at most, during which time the malicious validator can be kicked out by other validator votes.Minion: The minions are less-privileged nodes and cannot perform the verification work as validators in the PoA blockchain. In our system, each routing node is also a minion and has fewer rights in the PoA blockchain and it has a unique blockchain address, too. They can initiate token contracts, trigger some contract functions, and query the transaction information on the blockchain.Each blockchain system has a specific consensus algorithm to ensure the fairness of the blockchain transaction. In our blockchain network, we choose the PoA consensus algorithm which can process transactions more efficiently. In In our scheme, these nodes in WSN can be static or dynamic. For example, the server nodes in our solution are often static, while the routing nodes can be dynamic. However, the entry and exit of nodes does not affect our scheme, because the status information of our blockchain-based system is also updated dynamically.n unit tokens represent n unit corresponding packets. The essence of a token is the representation of the digitized information of the corresponding packets stored in the smart contract. The routing nodes can initiate token contracts to generate tokens and map the state information of related packets. They will make token transactions with each other via the token contract to transmit the tokens based on the sent and received packets. According to the consensus mechanism between server nodes, the token transactions cannot be revised arbitrarily by malicious nodes, to some extent, the token accurately represents the packet passed between the routing nodes.On the blockchain network, we use different blockchain tokens to represent the different packets to be delivered to the target nodes that Compared with traditional routing architectures, our system differs in that each routing node is registered on the registration contract after entering the blockchain-based routing network. The routing node will forward the packets to its next-hop routing node off the blockchain. Then they must confirm the routing information on the blockchain including the address of the next-hop routing node, the number of packets sent to the next node, and the timestamp. Then the routing information will be confirmed by the server nodes through the blockchain consensus mechanism and updated on the blockchain. The learning model of each routing node will pull this information from the blockchain and feed back the subsequent routing policy to the routing node. In the following subsections, we will introduce the specific blockchain network implementation and the routing policy generation method in detail.To effectively operate the blockchain-based routing network architecture, information related to the routing network needs to be transferred to the blockchain network. The related routing information is recorded in the smart contracts including the registration contract, the token contract, and blockchain transactions, i.e., the token transaction as shown in state is also an inherent variable that stores the state of whether a node is registered or not. When a new node wants to register on a registration contract, it should trigger the contract as a contract caller. It then inputs its physical address All the smart contracts are manipulated by the authenticated servers nodes and the results of the execution are returned to the blockchain network. The registration contract records the identity information  of all the routing nodes and server nodes to facilitate the query of the entire network node. The specific procedure for generating the registration contract is described as Algorithm 1. The mapping Algorithm 1 Procedure of registering a node. 2: Mapping  3: whiledo /* The contract is waiting for a contract caller to trigger */ Input: Contract Caller’s Blockchain Address Output: Registration Result r;  4:    5:  ifthen 6:      7:  else 8:     9:     10:     11:  end if 12: end while 1: Mapping R to the token balance (i)p and in q, i.e., p and q. First, n unit data packets to We initialize the number of packets in (ii)n of tokens sent to n is released on the token contract. While In our scheme, we put the process of validating the routing process on the blockchain network and all the routing nodes get the relevant routing information from the blockchain network instead of their neighbor routing nodes. After the packets are transmitted, (iii)n equals Then the token contract checks whether Each routing node can release a token contract to generate a certain number of tokens by giving the correlated variables on the blockchain ; the relMeanwhile, every token transaction is recorded on the blockchain by an authenticated server node, that all the routing nodes and server nodes have a back-up of the transaction recorder available for traceability. Each token transaction records the token name, the timestamp E represents a real routing environment, where the routing nodes update their states and make decisions about actions. State x represents the current position of the packets that need to be transferred, i.e., the packets locate currently at the x-th routing node. It means that the learning model has n states if there are n routing nodes. Policy a to be taken from the current state x and the action a represents forwarding the packets to the next-hop routing node m next hop nodes to choose from, the current state has m actions. Therefore, the action space i. For example, In this part, we elaborate the reinforcement learning-based routing scheduling scheme by using the global, dynamic, and trusted routing information provided by the proposed blockchain platform. The information obtained by the learning model from the blockchain network includes the timestamp Algorithm 2 Reinforcement learning and blockchain based (RLBC) routing algorithm.Input: Environment E; Action Space Output: Policy  1: x,a) = 0, P =  2: x =  3: fordo 4:   a = x);  5:   r = reward by routing action a;  6:   a;  7:    8:   x,a) = x,a) + r+x,a));  9:   x) = arg x,x, 10:   x =  11: end forIn the core Equation  at line r is an important parameter to motivate the routing nodes to make better routing decisions. Its value is determined by the routing information obtained by the learning model from the blockchain. The information includes the timestamp a, the number of nodes and packets it finally reaches is used to measure the reward. In a time slot, the value of r is r to r is related to the amount of successfully delivered tokens, for example, if the amount of tokens actually transmitted is r is r is determined by the amount of tokens delivered, it also restricts the non-forwarding behavior of malicious nodes in the threat model. If a malicious node doesn’t transmit any packet (token) in the routing process, the value of r will be small and it can greatly reduce the possibility that packets will pass through this malicious node. Thus, the problem of blocking routing link caused by malicious black hole nodes is greatly reduced.Parameter x and the number of a in the action space is K, the probability distribution of x) = By dynamically extracting and learning relevant routing information from the blockchain, the learning model will eventually output a policy Equation . The parThe reinforcement learning based routing algorithm helps the choice of routing by the nodes’ dynamic learning. Each hop of the routing information is recorded on the blockchain, and if the hop is looped, or the link is untrusted, or the transmit rate is low, the algorithm greatly reduces the probability that the packets pass through the link. At the same time, the routing algorithm can dynamically discover the more reliable and efficient routing links, so as to help the routing nodes make better routing decisions.PoA consensus mechanism: The blockchain network is based on a consensus mechanism called PoA (proof of authority), and only the validation of more than half of the authenticated server nodes can allow a server node to upload the transaction and update the routing information. Therefore, any information on the blockchain cannot be tampered with by individuals.Transaction traceability: The server nodes record the transactions on the blockchain, including the transaction of releasing token contract, the transaction of routing node running functions on the contract, the transaction of transferring tokens. All the information about these transactions is recorded on the blocks and can be traceable across the blockchain network.Routing information source: Different from the traditional routing network, in our scheme, all the routing nodes get the relevant routing information from the blockchain network instead of its neighbor routing nodes. In this way, the routing information obtained by the whole network routing nodes is consistent and not determined by individuals.Avoid the single point attacks: Our blockchain-based routing scheduling scheme does not require a trusted third-party central authority to manage routing information. The single point attack is prevented by the authentication of the transaction by multiple server nodes.No double-spending: The codes of our token contract specifies that each routing node address maps to only one address at each time slot, and that the routing node will not initiate token transactions to two other routing nodes at the same time slot.r of the routing link is very low based on our RLBC routing algorithm and the learning model will adaptively select the routing link of normal nodes.Self-adaptability: In the proposed routing scheduling scheme, the routing link with malicious nodes will not generate routing transactions. The reward value We established a trusted routing information management system based on the PoA blockchain, where all the routing nodes and server nodes jointly maintained the routing transaction. In this subsection, we analyze it from six perspectives to show how our proposed scheme ensures the security of the system. The related security performance is shown as follows:To evaluate the effectiveness and performance of our proposed approach, we implemented a prototype and compared its performance of the RLBC algorithm with other routing algorithms. In terms of effectiveness evaluation, it is mainly compared with the state-of-the-art reinforcement learning-based routing algorithm, the trust-based algorithm, and our original blockchain-based algorithm. For performance evaluation, we compared our system with the traditional PoW-based blockchain system to reflect the performance of our system in terms of delay, consumption and throughput.We built a PoA consortium blockchain and simulated 32 virtual servers to update blockchain transactions on the chain. All the routing information required by the reinforcement learning model can be obtained from the public blockchain transactions. The consortium blockchain was built based on Geth 1.8.19 which can provide reliable Ethereum transaction services. We chose the BP routing algorithm as the benchmark of performance comparisons . To simu(i)A malicious node releases a fake low (10% of the true amount) queue length information, but it transmits packets to other routing nodes.(ii)A malicious node releases the true queue length information, but it doesn’t transmit any packet to other routing nodes.(iii)A malicious node releases a fake low queue length information and it doesn’t transmit any packet to other routing nodes.To test the effectiveness, we set delay performance experiments in the routing environment with malicious nodes. In order to establish the comparative experiments, we compared our RLBC algorithm with the traditional BP algorithm, the trust-based backpressure (TB-BP) algorithm , the staTo evaluate the effectiveness of our system, we tested the average latency, average energy consumption and throughput of the blockchain token transactions in comparative experiments. We implemented and compared the traditional blockchain system based on PoW consensus mechanism to show the advantages of our system based on PoA consensus mechanism .We compared the traditional BP algorithm, QL-BP algorithm, and TB-BP algorithm with our system to see whether the routing scheduling schemes can be affected by malicious nodes. We introduced the BP algorithm combined with the blockchain-based (BC) architecture for comparison. The comparative experiments showed the differences between BP, QL-BP, TB-BP, proposed BC and proposed RLBC algorithm in the malicious routing environment. Compared to our system, the other three routing scheduling schemes do not have the addition of a blockchain structure, but the number of malicious nodes and the cheating methods are the same in all of these schemes.From We also implemented the comparative experiments in the routing environment with 50% malicious routing nodes, and the average packet delay of each algorithm was shown in In the efficiency experiment, for a more intuitive comparison, we compared our blockchain system based on the PoA consensus mechanism with the traditional PoW-based blockchain system. We recorded the experimental data such as transaction latency, energy consumption and throughput during the experiment.We took the transaction packaging time as the evaluation element of the average token transaction latency, which records the elapsed time that miners put the Ethereum token transaction on the blockchain. We recorded the token transaction latency of PoA and PoW blockchain systems with the increase of arrival rate The experiment results are shown in In Ethereum networks, “gas” is a special unit used to measure how much “work” an action or a series of actions of a miner has. It is determined by the number of computer instructions operated by the Ethereum transaction , and the gas fluctuation does not change much. For example, to calculate a Keccak256 encryption hash value, every time computing the hash will need 30 gas. Ethereum platform trading or contract execution of every operation needs a certain amount of gas, and the more computational resources operation we need, the more gas costs. Then the gas will be converted into corresponding ether currency to pay the blockchain miner. We take the gas consumption as the main evaluation index of the system energy consumption and the experimental results are shown below.As can be seen from the experimental results in The token transaction throughput shows the blockchain system’s ability to handle concurrent token transactions. We tested the throughput of transactions processed by our PoA blockchain system and the PoW blockchain system under the token transaction request rate ranges from 0 to 5000 times/s. The experimental results are shown in As we can see, the token transaction throughput increases steadily as the rate of concurrent requests increases, and the curve gradually flattens out as the throughput reaches its peak. Finally, the throughput of our blockchain system based on PoA consensus mechanism is stable at 3300 times per second, while that of the traditional blockchain system based on PoW consensus mechanism is only about 1900 times per second. From the experimental results, we can see that a PoA-based scheme has more efficient transaction processing capacity in the face of high request concurrency. It is appropriate and correct to take PoA algorithm as the consensus mechanism algorithm of the blockchain system, and this PoA blockchain-based routing scheduling scheme can effectively cope with the situation of large concurrent requests in the routing environment.In this paper, we proposed a trusted routing scheme based on the blockchain and reinforcement learning to provide a trusted routing environment and improve the performance of the routing network. As a decentralized system, the blockchain network provides a feasible scheme for routing information management and a platform for reinforcement learning of routing scheduling. We use the blockchain token to represent the routing packets, and each routing transaction is released to the blockchain network through the confirmation of the validator nodes. By making every routing transaction recorder traceable and tamper-proof, routing nodes can obtain dynamic and trusted routing information on the blockchain network. We also describe the detailed reinforcement learning model to adaptively choose the best routing path and avoid the routing links with malicious nodes. Finally, we carry out simulation experiments, the experimental results show that our system can effectively suppress the attacks of malicious nodes, and the system’s latency and throughput performance are excellent.In the future, we plan to use our system for experiments in more routing scheduling algorithms besides BP algorithm to verify the effectiveness and portability of our system. We also plan to incorporate the blockchain-based data validation technology into our work ,40,41,42


A vibration excitation system (VES) in a form of an active coupling is proposed, designed and manufactured. The system is equipped with a set of piezoelectric stack actuators uniformly distributed around the rotor axis and positioned parallel to each other. The actuator arrangement allows an axial displacement of the coupling halves as well as their rotation about any transverse axis. Through the application of the VES an aimed vibration excitation is realised in a co-rotating coordinate system, which enables a non-invasive and precise modal analysis of rotating components. As an example, the VES is applied for the characterisation of the structural dynamic behaviour of a generic steel rotor at different rotational speeds. The first results are promising for both stationary and rotating conditions. New developments in the turbo-machinery industry lead to constantly increasing demands on the economic and ecological efficiency and on the reliability of rotating components. However, the required increased performance of those components leads to higher dynamic requirements, which can be achieved with a better understanding and characterisation of the dynamic behaviour under rotating conditions.In particular for the development of high performance rotors such as fan blades of aircraft engines and turbine shafts for steam turbines a thorough analysis and understanding of the dynamic characteristics are of high relevance. In this context, a defined vibration excitation is an important prerequisite for the accurate, in-depth experimental determination of the structural dynamic behaviour of rotors. An analysis of the natural frequencies, modal damping and shapes is essential for the development of high performance rotors and can also be included in the monitoring of their structural state. In most cases the operational rotor excitation cannot fulfil the requirements for such an analysis. Since these properties are greatly dependent on the rotational speed, an excitation method of different degrees of freedom in a co-rotating coordinate system is of particular interest.Unbalanced, misaligned and mistuned components can cause vibrations which could lead to undesirable dynamic load on the system, flow separation and turbulence. This is especially evident in the case of aero-engine turbines and power energy systems.In addition, the operation at critical speeds is always associated with increased vibration amplitudes that can lead to unsteady flow conditions and an accompanying increased sound radiation. Therefore, a characterisation of the dynamic behaviour of rotors under different rotational speeds is an important factor for the optimal design and safe operation of rotors. Specifically, new experimental methods appropriate for the investigation of complex phenomena such as non-linear behaviour, gradual damage behaviour, internal friction and damping describing the structural behaviour of metallic and composite rotors are required ,2. TheseThe aim of the described investigation is to develop a novel excitation method enabling an on line vibration excitation for a non-invasive experimental modal analysis. To achieve this, a vibration excitation system is proposed that can be utilised for an online aimed excitation of rotors. Based on external sensor system and control software, a set of co-rotating piezo-stack actuators integrated to the VES is excited in a predefined way. The novelty here is that the piezo-stack actuators are positioned parallel to the rotational axis and they rotate along with the investigated rotor, which is an experimental approach that has not yet been reported.The paper is divided as follows. In In For the experimental modal analysis of rotating components several methods are known. These methods combine different excitation and measurement techniques for the identification of modal properties either at a standstill or at rotating conditions.The excitation methods for rotors can be classified according to the position of the excitation, resulting either in stationary excitation or in a co-rotating excitation. Several excitation methods are state-of-the-art. These include air jets, electromechanical shakers, active magnetic bearings, piezo-stack actuators, permanent magnets, non-contact laser ultrasonic excitation, eddy current systems and macro fibre components (MFC) ,5,6,7,8.In general, for a non-invasive base excitation the main disadvantage is the low force provided, e.g., by active magnetic bearings, in comparison to the force provided by piezo-stack actuators . The impFor the vibration measurement of rotating structures several systems are available. A well-known non-contact measurement method based on the stationary coordinate system is the blade tip-timing method, which evaluates time-dependent differences of blade passages after multiple rotor revolutions, a known method first used in 1970’s, ,23,24,25Another standard measurement approach is the application of strain gauges and lately fibre Bragg grating at rotors, which measure the vibration characteristics continuously during the rotation. The strain gauges are attached to points of reference at selected blades where frequencies and amplitudes of blade vibrations are determined by measuring strain dynamic components. However, the static position of the blades, which can provide significant diagnostic information about the possible damage of the blade, cannot be determined with strain gauge systems. Another drawback is the fact that in the aggressive environment of the steam turbine, the strain gauges experience a short lifetime of usually several days or weeks. In addition, such systems can monitor only selected blades .The vibration measurement of a rotating component with a coupled system of a scanning laser vibrometer and an optical derotator delivers confident results , enablinThe optical derotator constitutes an autonomous device whose main elements are a rotating unit with a Dove prism, a precision driving and an adjustment unit . On the A versatile excitation system, suitable for investigations on different rotors is designed in a multi-material development approach dealing with the entire material, design, simulation, assembly and prototype testing chain. Requirements are identified from a previous concept  for seleThe demand of an online vibration excitation for the experimental investigations of different metallic and composite rotors has led to the concept of a co-rotating active coupling system and to a patent application . The patHere this concept is further elaborated and a co-rotating vibration excitation system is designed and equipped with a number of piezoelectric stack actuators uniformly distributed around the rotor axis and positioned parallel to each other, connecting two coupling halves The requirements for the development of the excitation system depend on the rotors’ boundary conditions, on the used test bench system and on the requirements from the existing scientific projects. Since the system is based on an existing design example, some requirements are to be considered as base features that only vary in their values. For example, the necessary preload of the actuators can be achieved through various design solutions. In addition, the coupling elements between the VES, rotor and test rig can be modified for each type of rotor - test rig combination, respectively.requirements for the design of the VES are listed below. First of all, the geometry must be as compact as possible, with a short length to reduce unbalance and bending of the VES. Furthermore, the VES kinematics have to be very accurately defined in the axial direction (z) and to tilting and pivoting  due to high acceleration values. The occurring forces have to be analysed, originating from the preload force of the piezo actuators and forces from existing imbalance. Furthermore, a proper selection of actuators that enable sufficiently high actuation forces for a reliable measurement of the rotor’s vibration response is required. A suitable material selection for the VES is also important for achieving an overall low mass combined with sufficient stiffness. This goal could be met by the application of composite material with high specific anisotropic stiffness and strength.The most important strain output is required from each actuator for the determination of the prestress and actuator position and has to be taken into consideration during the design phase. Furthermore, a redundant safety solution has to be designed and implemented for high static and dynamic loading within actuation range. Last but not least, manufacturability with small tolerances to reduce imbalance has to be taken into consideration during the design phase as well as a simple assembly with central cable feed.Beyond the above-mentioned basic requirements, additional requirements exist to assure for the proper operation of the VES. For example, a rotational speed is an important parameter since it determines the acting loads caused directly or indirectly by centrifugal forces. Another parameter is the maximal transmitted drive torque, resulting from the required angular acceleration and the moment of inertia of the coupled rotors. The rotor mass distribution determines the magnitude of the moment of inertia and the required bending stiffness of the entire system. Under dynamic load the preload force is necessary to protect the actuators from damage that can be caused by the inertia of their own and coupled masses. Another important parameter is the axial rigidity  of the preload system, which determines the residual stroke and characteristic curve of the piezo actuator and should be a maximum of 30% of the actuator stiffness [assembly space, the diameter of the cable bushing is important, as the cables must be routed centrally along the axis of rotation in order to minimise possible sources of imbalance.Based on the given requirements the most important technical parameters are identified and taken into consideration during the design phase in order to ensure a proper functionality of the system. The maximum tiffness . The valpiezo actuators, the metallic elastic coupling element and the carbon fibre-reinforced epoxy bandaging of the actuators. The main configuration is a parallel connection of the actuators with one another forming an actuator group, and with a parallel connection of the actuator group with the elastic coupling element.The formulated requirements are solved by the proposed vibration excitation system, which is equipped with a set of piezoelectric stack actuators, The actuators are uniformly distributed around the rotor axis of rotation and are positioned parallel to each other. This arrangement allows both a high overall torsional stiffness (main power flow) and a favourable actuator pre-stress, which is characterised by a low stiffness of the coupling’s pre-load system. The arrangement also allows an axial displacement of the coupling elements as well as their rotation about any transverse axis. To allow a multi-directional excitation, the metallic elastic coupling element (8) is designed as a cylindrical steel hub incoorporating multiple flexure hinges , which pFor the operation of the piezo actuators, an excitation signal has to be generated in an external programmable signal generator and then enhanced by an amplifier. The acquisition of the necessary signals is performed by an external sensing system. In the described investigations, the Laser scanning Doppler vibrometer PSV-I-400 from the company Polytec  is used.Piezo stack actuators can be applied to act as linear electromechanical drivers . They arHereby, An actuator is suitable if its stiffness  model is used in order to adapt the stiffness. The decisive parameters for the design process are the width A numerical simulation of the excitation system was performed by means of a finite element model. The system was modelled in the commercial software ANSYS Workbench with the help of ANSYS Composite PrepPost. Selected degrees of freedom were constrained in order to simulate fixed boundary conditions at the rig adapter at position (2) The stress distribution for both the piezo actuators and the CFRP-bandage are numerically investigated using the same boundary conditions as previously presented. The piezo elements are wrapped with the CFRP bandage to reduce the bending caused from the centrifugal forces. The filament wound bandage was modelled with extruded Solid186 elements where six layers are used to represent the composite material. For the evaluation, the stress in fibre direction and transverse to the fibre direction as well as the Failure Mode Concept was used . The resFor a load case of 4000 N, an equivalent von-Mises stress of approximately 300 MPa is located at the inside edge of the coupling element, as shown in The actuators used here are stacks of several layers of thin piezoelectric discs, which are glued together. Due to this fact they exhibit a fragile and brittle behaviour when exposed to tensile loads parallel to the central axis and bending forces perpendicular to its central axis. Highly dynamic loads can also result in degradation of the adhesion inside the bonds. Therefore, to prevent any damage to the actuators, a preload has to be applied and the actuators are not allowed to experience any bending. After the assembly, a pre-load was applied on the piezo actuators using a screw mechanism and subsequently the actuators were checked for their functionality.The piezo actuators (PSt 1000/16/60 from Piezosystem Jena GmbH) had to be prepared for the integration in the piezo mounting system. As the piezo stack actuator has no defined outer diameter in the delivered condition, an embedding tool was used to surround the piezo element with cast resin to create a defined cylindrical surface, as shown in The piezo carrier as well as the piezo-stack actuators are wrapped with CFRP, shown in As the VES is exposed to dynamic loads, it was experimentally tested during operation in order to assess the piezo actuators. Then, the structural functionality of the whole system is investigated, in particular to examine the durability of the composite assembly and of the coupling element.At first, the system had to undergo a rotational endurance test in order to evaluate its resilience under high centrifugal loads. These loads lead to different loading conditions depending on the position and geometry of each specific part. For instance, the coupling element exhibits an expansion similar to a pressure vessel, due to the reduced stiffness in its middle section, whereas the piezo actuators exhibit bending loads, due to their eccentric position. For this purpose, the system was mounted onto a test bench and stepwise exposed to several rotational speeds. The VES has been accelerated with 2 rad/sThe results of the modal analysis are presented in The relative change An experimental modal analysis has been performed in order to investigate the structural dynamic behaviour of the generic rotors. For this purpose, this generic steel rotor was excited and the vibration response was measured under stationary conditions and at different rotational speeds. The tests were performed under stationary conditions and during rotation up to 2000 rpm. The rotor is shown in The acquisition of the vibration response was carried out by employing the POLYTEC laser scanning vibrometer (LSV) in combination with the POLYTEC optical derotator, that is shown in The general set-up involves the PSV-I-400 optical scanning head equipped with the highly sensitivity vibrometer sensor (OFV-505), OFV-5000 controller, PSV-E-400 junction box and the acquisition computer system. In addition, a single-point vibrometer was used to deliver a reference signal of the dynamic structural behaviour. The excitation signal was created in the PSV software and then generated and transformed by an amplifier that was driving the piezo actuators. The tests were performed under environmental conditions and under technical vacuum. During the tests, 80 measurement points were selected as shown in The analysed eigenfrequencies of the generic rotor at 0, 1000, 1500 and 2000 rpm are shown in A novel co-rotating vibration excitation system has been designed and manufactured, which allows the realisation of a non-invasive modal analysis of rotating components. The co-rotating vibration excitation system is capable of exciting rotors under rotational conditions for a determination of speed-dependent modal parameters. The results show the capability of the system under different rotational speeds on the example of a generic metallic rotor. The system was validated under non-rotating and rotating conditions using a Scanning Laser Doppler Vibrometer mounted on an optical derotator and aligned coaxially to the rotor by using a Dove prism. The novelty of this method lies to the fact that a set of piezo-stack actuators are positioned parallel to the rotational axis and they rotate along with the investigated rotor, which has not yet been reported.A number of further investigations are under consideration and will be published in the near future. First of all, the behaviour of the excitation system should be further investigated including different types of metallic rotors and by testing real rotor-bladed discs in order to identify the application limits of the current solution. Specifically, in the case of composite rotors it is important to investigate whether the transferred energy to the rotor is sufficient due to the increased material damping and coulomb damping, e.g., between compressor blades and metal rotor hub. A further development of the high-power electronics is required in order to realise other types excitation such as sweep, sine, burst random and white noise excitation in addition to the impulse excitation used so far. Due to project and resources limitations, the current electronic system does not allow for an individual control of the piezo actuators. The required power supply of the excitation system is a challenge, as only few slip rings exist that can achieve rotational speeds up to 10,000 rpm. However they are relative expensive and also limited in the power supply. It is however possible to supply each piezo actuator using a separate channel form the same slip ring. Electrical disturbance and limitations do exist but it is possible at least under laboratory conditions. The application of the method to larger rotors supported in two or more bearings still is under investigation, with an idea of adjusting the system also for these cases.Moreover, the operational stability as well as the fatigue of the system was not investigated, especially the behaviour of the piezo actuators and the CFRP-bandage under long-term testing and has to be also conducted. The vibration excitation system has to be compared to different excitation methods in order to determine the advantages and drawbacks of the current system. The possibility to scale up the co-rotating excitation system has to be considered for larger rotor systems and also possible limitations of hardware solutions due to complex loading conditions and high rotational speeds have to be taken into account. Last but not least, the current proposed system can be used as basis for the development of a co-rotating vibration excitation system for metallic and composite drive shafts.In general, the proposed solution can fulfil a vibration excitation of a rotor during operation and combined with a laser scanning vibrometer mounted on an optical derotator can perform an experimental modal analysis under different rotational speeds.


In this work, we aim to classify a wider range of Electromagnetic Interference (EMI) discharge sources collected from new power plant sites across multiple assets. This engenders a more complex and challenging classification task. The study involves an investigation and development of new and improved feature extraction and data dimension reduction algorithms based on image processing techniques. The approach is to exploit the Gramian Angular Field technique to map the measured EMI time signals to an image, from which the significant information is extracted while removing redundancy. The image of each discharge type contains a unique fingerprint. Two feature reduction methods called the Local Binary Pattern (LBP) and the Local Phase Quantisation (LPQ) are then used within the mapped images. This provides feature vectors that can be implemented into a Random Forest (RF) classifier. The performance of a previous and the two new proposed methods, on the new database set, is compared in terms of classification accuracy, precision, recall, and F-measure. Results show that the new methods have a higher performance than the previous one, where LBP features achieve the best outcome. Condition monitoring of High-Voltage (HV) equipment in power generating plants is essential as any defect puts at risk staff safety as well as the power plant’s operation. Electromagnetic Interference (EMI) is generated due to the presence of electrical or mechanical faults in various equipment types, such as motors, transformers, generators, and switchgear. Conducted and radiated EMI are exploited by EMI experts to gain information on faults type. Consequently, electrical insulation degradation can be identified through EMI diagnosis ,2. PopulPreviously, authors in  developeFault detection using the machine learning approach has been addressed in many research works . The worA variety of signal processing techniques have been applied to the phase-resolved PD data. For instance, in , the autThe image processing technique gained the attention of many researchers in the field. In , waveletStatistical measures such as mean, variance, skewness, kurtosis, cross correlation, etc. were calculated as features on the phase-resolved data of five PD defect types in gas-insulated switchgear  and in sLittle attention was given to pulse shape features in the literature until the early 2000s. This method selects the characteristics related to PD pulse shape such as rise and fall time, area, pulse width, and magnitude . The autFeature extraction methods belonging to the discussed groups have also been combined. For example, in , the autThere is no doubt that the previous work in the literature is of good quality and has been successful. However, the work is limited to laboratory data measured using sensing methods that differ from EMI measurement techniques . In thisThe paper is structured as follows. The next section summarises the work from data acquisition to model development. The EMI measurement technique is described in EMI measurement and diagnosis can be used to detect Radio Frequency (RF) energy propagation due to insulation defects in various electrical assets including cables, motors, and generators . The proThis section describes the concept of GAF, LBP, and LPQ algorithms utilised as feature extraction techniques and RF classification algorithms, that are implemented in the proposed approach. Two variations of the GAF algorithm are studied in this work and are described in the following section.n real valued observations in a time series This section introduces two types of GAF algorithm, known as the Gramian Angular Summation Field (GASF) and the Gramian Angular Difference Field (GADF), which are techniques that encode the time series signals into an image. The concept here is to transfer the time series to a polar coordinate space. The Gramian matrix is then formed where each element is calculated by the cosine of the summed angles for GASF or the sine of the subtracted angles for the GADF. This is explained mathematically as follows. First, let vectors be denoted by bold lower case, scalars by lower case and matrices by bold upper case. For r, from the time stamp t, as presented in Equation ϕ=cosN is a constant used as a regularisation factor for the polar space span, and is set to In Equation , N is a rding to . The polEquation ) betweenEquation . These aquations  and 4)..N is a cThe GADF algorithm is similar to the GASF except that GADF is constructed using the trigonometric difference of the inverse sine as follows:The constructed gorithms .This algorithm is designed for image processing, and it exploits phase information computed from the Fourier phase spectrum of the image. The phase of four low-frequency components are mapped to code words which are embedded in a histogram of features for classification.u and v, is performed to retrieve phase information for each pixel of coordinates x and y, and is calculated on a p is the number of pixels, using the following equation:Let In the LPQ calculation, only the phase information, at the first four frequency coefficients . The fiBy separating the real and imaginary parts of Next, the real and imaginary values are quantised using the criteriaThese values are then encoded using binary coding through Equation (9), which provides values in the range of [0–255].Here, the summation is performed on the quantisation of each real and imaginary of the four first frequency coefficients   of the nBased on the best split point, split the main node into child nodes and reduce feature instances dimension along the nodes.Repeat Steps 1–3 until a maximum depth Repeat Steps 1–4 for formance .The steps for training an RF classifier are as follows.EMI signals were measured, with a sampling rate of 24 kHz and in microvolts, on operating sites using the EMI technique. An EMI expert analysis of these signals was followed in order to label the type of discharge source present in each signal. The outcome of this analysis revealed a total of nine discharge types denoted as Arcing (A), Corona (C), Data Modulation (DM), PD, Process Noise (PN), Random Noise (RN), Exciter (E), minor PD (mPD), and micro Sparking (mS). It is important to note that minor and micro define the discharge level and repetition rate. Details on the EMI signals, including the number of files per dishcarge source, the recorded duration, the asset at which the signal was recorded, and the total samples used for training and testing, obtained through signal segmentation, are presented in Divide each time series signal into segments of 2000 samples for ease of GAF computation.Map each time series segment to an image using GASF and GADF algorithms.Resize the images to Calculate LPQ and LBP histograms from each image to extract the important features and non-redundant information.Implement the feature histograms with associated labels in the RF classifier.First, a previously developed feature extraction technique, called ALIF-Entropy, along with a Multi-Class Support Vector Machine (MCSVM) (see ), was aps papers ,43 in ors papers . This aps papers .Randomly shuffle the dataset.Split the dataset into 10 groups, in that each group contains samples from each of the 9 classes. For each individual group:Leave the group for testing and use the remaining ones for training.Train and test the model and obtain the classification accuracy.Discard the model, save the accuracy for this fold, and repeat Steps 3–5.Calculate the average accuracy across the saved accuracy ξ1−10 in  from eacThe steps to performing the 10-fold cross validation method are as follows.Here, each data instance is allocated to a group and remains in the group during the 10-fold cross validation procedure, see F) [The classifier’s performance is evaluated in terms of the most popular evaluation metrics in machine learning, which are classification accuracy  ,49, whicd papers ,51. The The evaluations were performed using MATLAB R2017a in a CPU 4 Gb RAM computer.This section shows and compares the classification findings performed using the proposed feature extraction and classification techniques in this paper and the previously applied technique. The results are presented in The confusion matrices of the three approaches are shown in In order to assess the performance of the proposed method with varied window sizes for the signals segmentation, classification results, including the CI of the accuracy across all folds calculated using Equation , where 1The proposed feature extraction algorithms demonstrate the ability to extract relevant fingerprint of the discharge sources and noise signals, which benefits the classification process, and thus provides the potential of automatic EMI diagnosis. The factors that positively influence the classification are as follows. First, setting a number of trees in the random forest to 500 improves the classification accuracy, as it was reported previously that the larger number of trees yield a higher performance. This was confirmed in our analysis where lower classification accuracy was achieved when employing 150 trees only. Second, the analysis of the proposed approach on various window size segments revealed that employing a window of 2000 samples benefits the practicability of this approach with reference to classification performance and computation.Class imbalance is one of machine learning limitations. This means that the total number of data in some classes is far more than the number of data in others. This issue affects the performance of the proposed GAF-LBP and GAF-LPQ methods as well as the previously proposed ALIF-entropy method, where the support vector machine algorithm does not converge during the training stage. This limitation will be addressed in future work. The authors are currently looking at modifying the cost function in the classification algorithm in order to solve this issue. Furthermore, a weighted version of random forest or support vector machine algorithms will also be considered. Another factor that may affect the discharge signals classification is noise. As the noise level increases in a signal related to a particular fault, the feature extraction and classification could be more challenging. Denoising techniques could be considered for highly noisy signals representing a discharge source. A typical approach would be to measure the noise level in a signal and compare it to a particular threshold. If the noise level falls below the threshold, then the signal should be denoised. To summarise, class imbalance and noise negatively influence the performance of the proposed approach.This work introduces a new algorithmic approach to EMI discharge source classification that improves upon previous work for an increased number of discharge source types. The novel approach is based on time series imaging using the GAF method combined with image-based feature extraction techniques known as LBP and LPQ. The algorithm performance was evaluated using the 10-fold cross validation approach and the mean, variance, and CI of the classification accuracy across all folds. Classification results show a successful improvement compared to previous work using both techniques, and LBP-based work achieved the best performance. One limitation in the findings was the confusion of PD and mS signals. Investigation on other aspects of the measured signal, such as frequency or denoising, should be considered in this case. Despite this drawback, the proposed approach could significantly aid in and contribute to an easier and faster method of EMI diagnostics compared to the traditional method. To conclude, the gain in classification performance is a gain in confidence and a motivation to consider the implementation of this approach in an EMI diagnosis instrument. Future work will consist of the full cross validation of the developed algorithms with synthetically generated laboratory captured and real world data sets in the presence of different noise types and levels, as well as other sources of interference, which could lead to a multi-label problem, where multiple EMI sources could be identified in a single signal that carries more than one EMI source. Statistical analysis on how noise affects the algorithm performance will also be employed.