Showing papers in "IEEE transactions on industrial electronics in 2022"

Modified Deep Autoencoder Driven by Multisource Parameters for Fault Transfer Prognosis of Aeroengine

Abstract: The existing fault prognosis techniques of aeroengine mostly focus on a single monitoring parameter under stable condition, and have low adaptability to new prognosis scenes. To boost the fault prognosis capability cross aeroengines, modified deep autoencoder (MDAE) driven by multi-source parameters is proposed in this article. First, the sensitive multi-source parameters are selected and fused using linear local tangent space alignment to define a fused health index (FHI) to characterize performance degradation of aeroengine. Second, MDAE model is constructed with adaptive Morlet wavelet to flexibly establish accurate mapping hidden in the FHI under analysis. Third, parameter transfer learning is used to provide good initial parameters for enabling the constructed MDAE to have cross-domain fault prognosis capability. The proposed method is used to analyze both the simulation multisource performance degradation parameters of aeroengines (system level) and experiment run-to-failure bearing datasets (component level). The results confirm the feasibility of the proposed method in cross-domain fault prognosis of aeroengines, which outperforms the existing methods.

Approximation-Free Robust Synchronization Control for Dual-Linear-Motors-Driven Systems With Uncertainties and Disturbances

Abstract: This article addresses the synchronization control problem for dual-linear-motors-driven systems with model uncertainties and disturbances. An approximation-free robust synchronization control scheme based on cross-coupled control (CCC) frame is proposed to achieve high-precision tracking and synchronization performance, along with excellent uncertainties and disturbances rejection ability. More specifically, the CCC frame is designed to handle the asynchronous motion of two parallel motors. The main advantage is that the proposed method does not require the explicit system model, and any approximations utilized to handle the model uncertainties, such as estimation, identification, and online learning, are not required. Therefore, the computational burden and complexity of the controller are significantly reduced. Considering the importance of the transient and steady-state response, the concept and technology of prescribed performance are adopted to guarantee the control effect and state constraints. In addition, none of the high-order derivatives of desired trajectory, difficult to obtain directly in many applications, are used in the proposed controller. Furthermore, the stability and convergence performance of the closed-loop system are rigorously demonstrated. Finally, comparative experiments show the effectiveness of this study via a dual-driven H-type gantry.

Deep Deterministic Policy Gradient-DRL Enabled Multiphysics-Constrained Fast Charging of Lithium-Ion Battery

Abstract: Fast charging is an enabling technique for the large-scale penetration of electric vehicles. This article proposes a knowledge-based, multiphysics-constrained fast charging strategy for lithium-ion battery (LIB), with a consciousness of the thermal safety and degradation. A universal algorithmic framework combining model-based state observer and a deep reinforcement learning (DRL)-based optimizer is proposed, for the first time, to provide a LIB fast charging solution. Within the DRL framework, a multiobjective optimization problem is formulated by penalizing the over-temperature and degradation. An improved environmental perceptive deep deterministic policy gradient (DDPG) algorithm with priority experience replay is exploited to tradeoff smartly the charging rapidity and the compliance of physical constraints. The proposed DDPG-DRL strategy is compared experimentally with the rule-based strategies and the state-of-the-art model predictive controller to validate its superiority in terms of charging rapidity, enforcement of LIB thermal safety and life extension, as well as the computational tractability.

Asynchronous Particle Swarm Optimization-Genetic Algorithm (APSO-GA) Based Selective Harmonic Elimination in a Cascaded H-Bridge Multilevel Inverter

Abstract: In this article, a hybrid asynchronous particle swarm optimization-genetic algorithm (APSO-GA) is proposed for the removal of unwanted lower order harmonics in the cascaded H-bridge multilevel inverter (MLI). The APSO-GA is applicable to all levels of MLI. In the proposed method, ring topology based APSO is hybrid with GA. APSO is applied for exploration and GA is used for the exploitation of the best solutions. In this article, optimized switching angles are calculated using APSO-GA for seven-level and nine-level inverter, and results are compared with GA, PSO, APSO, bee algorithm (BA), differential evolution (DE), synchronous PSO, and teaching–learning-based optimization (TLBO). Simulation results show that APSO-GA can easily find feasible solutions particularly when the number of switching angles is high; however, the rest of all stuck at local minima due to less exploration capability. Also, the APSO-GA is less computational complex than GA, BA, TLBO, and DE algorithms. Experimentally, the performance of APSO-GA is validated on a single-phase seven-level inverter.

Co-Estimation of State-of-Charge and State-of- Health for Lithium-Ion Batteries Using an Enhanced Electrochemical Model

Abstract: Real-time electrochemical state information of lithium-ion batteries attributes to a high-fidelity estimation of state-of-charge (SOC) and state-of-health (SOH) in advanced battery management systems. However, the consumption of recyclable lithium ions, loss of the active materials, and the interior resistance increase resulted from the irreversible side reactions cause severe battery performance decay. To maintain accurate battery state estimation over time, a scheme using the reduced-order electrochemical model and the dual nonlinear filters is presented in this article for the reliable co-estimations of cell SOC and SOH. Specifically, the full-order pseudo-two-dimensional model is first simplified with Padé approximation while ensuring precision and observability. Next, the feasibility and performance of SOC estimator are revealed by accessing unmeasurable physical variables, such as the surface and bulk solid-phase concentration. To well reflect battery degradation, three key aging factors including the loss of lithium ions, loss of active materials, and resistance increment, are simultaneously identified, leading to an appreciable precision improvement of SOC estimation online particular for aged cells. Finally, extensive verification experiments are carried out over the cell's lifespan. The results demonstrate the performance of the proposed SOC/SOH co-estimation scheme.

OpenStreetMap-Based Autonomous Navigation for the Four Wheel-Legged Robot Via 3D-Lidar and CCD Camera

Abstract: OpenStreetMap (OSM) is widely used in outdoor navigation research recently, which is publicly available and can provide a wide range of road information for outdoor robot navigation. In this article, aiming at the problem that the map error of OSM will cause the global path to be inconsistent with the real environment, we propose an OSM-based robot navigation method that combines road network information and local perception information. As a global map, OSM provides road network information to obtain the global path by the Dijkstra algorithm. Multisensor (including 3D-LiDAR and Charge-coupled Device (CCD) camera) information fusion offers local information to detect local road information and obstacles for local path planning. We filter local road information and then extract useful road features to optimize the local path. Finally, this local path is used for robot path tracking to complete navigation tasks. The experimental results show that the average error between the trajectory of the robot and the road center is 0.18 m. This reveals that our method has high navigation accuracy and strong robustness in the real complex environment.

Sliding Mode Dual-Channel Disturbance Rejection Attitude Control for a Quadrotor

Abstract: In this article, a sliding mode dual-channel disturbance rejection control based on an extended state observer is proposed for the attitude control of a quadrotor under unknown disturbances. There exist an inner disturbance rejection channel (IDRC) and an outer disturbance rejection channel (ODRC) in this control scheme. In the IDRC, a low-frequency disturbance compensator is proposed to obtain the disturbance compensation value and to compensate the low-frequency component of the lumped disturbance. In the ODRC, a novel sliding mode controller with a variable-gain switching term and a constant-gain switching term is designed, and the switching terms are used to compensate the virtual disturbance estimation error and the high-frequency component of the lumped disturbance. The low-frequency and high-frequency components of the lumped disturbance can be estimated and the influence of the virtual disturbance estimation error is reduced by using the proposed control scheme. The stability of the system is proved by using the Lyapunov theory. Finally, the effectiveness of the proposed scheme is tested by numerical simulations and platform experiments.

Subdomain Adaptation Transfer Learning Network for Fault Diagnosis of Roller Bearings

Abstract: Due to the data distribution discrepancy, fault diagnosis models, trained with labeled data in one scene, likely fails in classifying by unlabeled data acquired from the other scenes. Transfer learning is capable to generalize successful application trained in one scene to the fault diagnosis in the other scenes. However, the existing transfer methods do not pay much attention to reduce adaptively marginal and conditional distribution biases, and also ignore the degree of contribution between both biases and among network layers, which limit classification performance and generalization in reality. To overcome these weaknesses, we establish a new fault diagnosis model, called subdomain adaptation transfer learning network (SATLN). First, two convolutional building blocks were stacked to extract transferable features from raw data. Then, the pseudo label learning is amended to construct target subdomain of each class. Furthermore, a subdomain adaptation is combined with domain adaptation to reduce both marginal and conditional distribution biases simultaneously. Finally, a dynamic weight term is applied for adaptive adjustment of the contributions from both discrepancies and each network layers. The SATLN method is tested with six transfer tasks. The results demonstrate the effectiveness and superiority of the SATLN in the cross-domain fault diagnosis field.

SuperGraph: Spatial-Temporal Graph-Based Feature Extraction for Rotating Machinery Diagnosis

Abstract: Vibration signals always contain noise and irregularities, which makes spectrum analysis difficult to extract high-level features. Recently, graph theory has been applied to spectrum analysis to improve the performance of feature extraction. By converting the raw data into graphs, hidden structural and topological information can be obtained. In this article, a spatial-temporal graph-based feature extraction, called SuperGraph, for rotating machinery fault diagnosis is proposed. Specifically, graph theory-based spectrum analysis is used to construct the spatial-temporal graph. Then, the Laplacian matrix-based feature vector is extracted from the constructed spatial-temporal graph. By this means, the spatial-temporal graph is converted into the one-dimensional (1-D) vector for further constructing SuperGraph, where each node of the SuperGraph represents a spatial-temporal graph and the SuperGraph is composed of many local graphs. In the local graph, only the same type of nodes are connected to form a fully connected graph. Thus, the task of graph classification can be transformed into classifying the nodes in the SuperGraph. After graph convolutional network is established for learning and obtaining deep features, the label of nodes is identified from a $softmax$ model. Experiments are conducted on two benchmarking datasets and a practical experimental platform to verify effectiveness of the proposed fault diagnosis method.

A Self-Reproduction Hyperchaotic Map With Compound Lattice Dynamics

Abstract: In this article, sinusoidal functions are introduced to a discrete map for hyperchaos generation and attractor self-reproduction. The constructed map shares a unique structure with controllable symmetry and conditional symmetry, which exhibits compound lattice dynamics, including 1-D and 2-D attractor growth. The direction of attractor growth can be controlled under polarity balance. STM32-based circuit realization verifies the results with numerical simulation and theoretical analysis. A pseudorandom number generator is built finally based on the newly proposed hyperchaotic map proving the high performance in application.

Real-Time Nonlinear Model Predictive Control of Active Power Filter Using Self-Feedback Recurrent Fuzzy Neural Network Estimator

Abstract: A real-time nonlinear model predictive control using a self-feedback recurrent fuzzy neural network (SFRFNN) estimator for an active power filter is developed to improve the performance of harmonic compensation. First, an SFRFNN with a recurrent structure and fuzzy rules is proposed as a prediction model for nonlinear systems. The SFRFNN merges the advantages of the fuzzy system and the recurrent neural network with a self-feedback structure, which can significantly improve the dynamic performance. Second, the optimization method based on gradient descent is employed to solve the optimal control problem. In addition, the convergence of the proposed SFRFNN and the stability of RT-NMPC are guaranteed using Lyapunov stability theory. Finally, the hardware experiment demonstrated that the proposed method has better performance in both steady and dynamic states compared with existing methods and RT-NMPC using a radial basis function neural network.

Quantitative Tuning of Active Disturbance Rejection Controller for FOPTD Model With Application to Power Plant Control

Abstract: Active disturbance rejection controller (ADRC) has achieved soaring success in motion controls featured by rapid dynamics. However, it turns obstreperous to implement it in the power plant process with considerable time-delay, largely because of the tuning difficulty. To this end, this article proposes a quantitative tuning rule for the time-delayed ADRC (TD-ADRC) structure based on the typical first order plus time delay (FOPTD) model. By compensating the FOPTD process as an integrator plus time delay in low frequencies, the gain parameter of TD-ADRC can be related to a scaled time constant which shapes the closed-loop tracking performance. Bandwidth parameter of extended state observer is scaled as a dimensionless parameter. A sufficient stability condition of TD-ADRC is theoretically derived in terms of the scaled parameter pair, the range of which falls within the practical interest. Relative delay margin is revealed as a critical robustness metric among others, a default pair of scaled parameter setting is recommended as well as an explicit retuning guideline according to the user's preference for performance or robustness. Simulation and laboratory water tank experiment validate the tuning efficacy and a coal mill temperature control test depicts a promising prospective of the proposed method in process control practice.

Disturbance-Immune and Aging-Robust Internal Short Circuit Diagnostic for Lithium-Ion Battery

Abstract: The accurate diagnostic of internal short circuit (ISC) is critical to the safety of lithium-ion battery (LIB), considering its consequence to disastrous thermal runaway. Motivated by this, this article proposes a novel ISC diagnostic method with a high robustness to measurement disturbances and the capacity fading. Particularly, a multistate-fusion ISC diagnostic method leveraging polarization dynamics instead of the conventional charge depletion is proposed within a model-switching framework. This is well-proven to eliminate the vulnerability of diagnostic to battery aging. Within this framework, the recursive total least squares method with variant forgetting is exploited, for the first time, to mitigate the adverse effect of measurement disturbances, which contributes to an unbiased estimation of the ISC resistance. The proposed method is validated both theoretically and experimentally for high diagnostic accuracy as well as the strong robustness to battery degradation and disturbance.

Distributed Resilient Secondary Control for DC Microgrids Against Heterogeneous Communication Delays and DoS Attacks

Abstract: In this article, a cooperative resilient control method for dc microgrid (MG) is proposed to dispel the adverse influences of both communication delays and denial-of-service (DoS) attacks. To avoid that the sampling period is captured by intelligent attackers, a new time-varying sampling period, and an improved communication mechanism are first introduced under the sampling control framework. Based on the designed sampling period and communication mechanism, a resilient secondary controller is designed. It is theoretically shown that the developed method can achieve the goals of bus voltage restoration and current sharing even in the presence of both DoS attacks and heterogeneous communication delays. Finally, a dc MG test system is built in a controller-hardware-in-the-loop testing platform to illustrate and verify the effectiveness of our developed method against both communication delays and DoS attacks.

A Single-Source Nine-Level Boost Inverter With a Low Switch Count

Abstract: Switched-capacitor-based multilevel inverters for boost-type dc–ac power conversions usually exhibit a trade-off between the switch count and switch-voltage rating, i.e., a reduction of one necessitating an increase of the other. Such a dilemma is well addressed in this article by proposing a novel single-phase nine-level inverter based on a switched-capacitor network with a single switch. The proposed inverter then reduces the number of switches while generating a boosted dc-link voltage. A unique six-switch full-bridge cooperating with a low-frequency half-bridge further steps-up the output voltage with a quadruple gain. The voltage stresses on the power devices are, however, maintained low even under the boosted high output voltage, as all the switches/diodes can be clamped to any of the low-voltage capacitors. Consequently, low-voltage power devices can be utilized, reducing the overall power loss. Detailed theoretical analysis, calculations, and design considerations of the proposed inverter are provided. Comparisons with the prior-art inverters illustrate its advantages. Simulations and experimental tests on a 1-kVA inverter prototype verify the above-claimed benefits.

Model Predictive Control Using Artificial Neural Network for Power Converters

Abstract: There has been an increasing interest in using model predictive control (MPC) for power electronic applications. However, the exponential increase in computational complexity and demand of computing resources hinders the practical adoption of this highly promising control technique. In this article, a new MPC approach using an artificial neural network (termed ANN-MPC) is proposed to overcome these barriers. A power converter with a virtual MPC controller is first designed and operated under a circuit simulation or power hardware-in-the-loop simulation environment. An artificial neural network (ANN) is then trained offline with the input and output data of the virtual MPC controller. Next, an actual FPGA-based MPC controller is designed using the trained ANN instead of relying on heavy-duty mathematical computation to control the actual operation of the power converter in real time. The ANN-MPC approach can significantly reduce the computing need and allow the use of more accurate high-order system models due to the simple mathematical expression of ANN. Furthermore, the ANN-MPC approach can retain the robustness for system parameter uncertainties by flexibly setting the input elements. The basic concept, ANN structure, offline training method, and online operation of ANN-MPC are described in detail. The computing resource requirement of the ANN-MPC and conventional MPC are analyzed and compared. The ANN-MPC concept is validated by both simulation and experimental results on two kW-class flying capacitor multilevel converters. It is demonstrated that the FPGA-based ANN-MPC controller can significantly reduce the FPGA resource requirement (e.g., 2.11 times fewer slice LUTs and 2.06 times fewer DSPs) while offering a control performance same as the conventional MPC.

Sliding Mode Control of Grid-Connected Neutral-Point-Clamped Converters Via High-Gain Observer

Abstract: In this article, a nonlinear high-gain observer (NHGO)-based second-order sliding mode (SOSM) control strategy is proposed for the three-phase three-level neutral-point-clamped (NPC) converter. This controller applies the advanced SOSM algorithm both in the voltage regulation loop and in the power tracking loop, which provides a fast dynamic for the dc-link voltage, and also assures a good steady-state behavior for the NPC converter. Additionally, an NHGO technique is implemented in the voltage regulator combining with the SOSM algorithm. The conventional observer-based controllers suffer from the destructive effects of measurement noise, and it can only be addressed by diminishing the observer gain, which sacrifices the observer property. The NHGO technique adopts a time varying gain, that is, high gain in transient while low gain in steady state, which minimizes the adverse influence of measurement noise. The tuning method of the proposed NHGO-based SOSM controller is given to simplify the implementation process. Finally, the simulation and experimental results of the proposed control scheme for the NPC converter are given and compared with the conventional PI controller as well as the well-known linear extended state observer-based control method, which validates the feasibility and superiority of the proposed controller.

Adaptive Attitude Control of a Quadrotor Using Fast Nonsingular Terminal Sliding Mode

Abstract: As one type of unmanned aerial vehicles, the quadrotor typically suffers from payload variations, system uncertainties, and environmental wind disturbances, which significantly deteriorate its attitude control performance. To provide high-speed, accurate, and robust attitude tracking performance for the quadrotor, an adaptive fast nonsingular terminal sliding mode (AFNTSM) controller is proposed in this article. The proposed AFNTSM controller combines the advantages of fast nonsingular terminal sliding mode (FNTSM), integral sliding mode, and adaptive estimation techniques, which are effective to achieve the desired tracking performance and suppress control signal chattering. Furthermore, unlike conventional methods, the adaptive estimation removes the requirements for the upper bound information of the disturbances. It is proved that the proposed AFNTSM can guarantee finite-time convergence and zero tracking error for the quadrotor attitude control. Finally, comparative study with the FNTSM control only and conventional sliding mode control is conducted through experiments and the results demonstrate that the proposed AFNTSM can achieve faster convergence and stronger robustness in line with theoretical analysis.

Approximate Cost-Optimal Energy Management of Hydrogen Electric Multiple Unit Trains Using Double Q-Learning Algorithm

Abstract: Energy management strategy (EMS) is the key to the performance of fuel cell / battery hybrid system. At present, reinforcement learning (RL) has been introduced into this field and has gradually become the focus of research. However, traditional EMSs only take the energy consumption into consideration when optimizing the operation economy, and ignore the cost caused by power source degradations. It would cause the problem of poor operation economy regarding Total Cost of Ownership (TCO). On the other hand, most studied RL algorithms have the disadvantages of overestimation and improper way of restricting battery SOC, which would lead to relatively poor control performance as well. To solve these problems, this paper establishes a TCO model including energy consumption, equivalent energy consumption and degradation of power sources at first, then adopt the Double Q-learning RL algorithm with state constraint and variable action space to determine the optimal EMS. Finally, using hardware-in-the-loop platform, the feasibility, superiority and generalization of proposed EMS is proved by comparing with the optimal dynamic programming and traditional RL EMS and equivalent consumption minimum strategy (ECMS) under both training and unknown operating conditions. Results prove that the proposed strategy has high global optimality and excellent SOC control ability regardless of training or unknown conditions.

A Super-Twisting-Like Fractional Controller for SPMSM Drive System

Abstract: A new super-twisting-like fractional (STLF) controller is proposed in this article to improve the control performance for surface-mounted permanent magnet synchronous motor (SPMSM) system. Compared with the conventional super-twisting sliding mode (STSM) algorithm, the most distinctive characteristic of the proposed strategy is to replace the discontinuous switching function hidden under the integration with a nonsmooth term, which can significantly optimize the performance of SPMSM system. Meanwhile, the introduction of nonsmooth term can provide stronger anti-disturbance capability than the widely used smooth controller. In addition, the desired control performance can be achieved by adjusting the parameters of the proposed STLF controller according to the specific situations. Comparative experiments among the proposed STLF, conventional STSM and proportional integral controllers are carried out to demonstrate the feasibility and effectiveness of the proposed STLF technique.

Recursive Correlative Statistical Analysis Method With Sliding Windows for Incipient Fault Detection

Abstract: This article proposes a new combination of a correlative statistical analysis and the sliding window technique to detect incipient faults. Compared with the existing monitoring methods based on principal component and transformed component analyses, the combination fully uses the information from the process and quality variables. The sliding window, however, inevitably increases the computational burden due to the repeated window calculations. Therefore, a recursive algorithm is proposed in this article, which has been shown to have less calculation complexity. Furthermore, a randomized algorithm is proposed to determine the width of the sliding window. A numerical example and the thermal power plant process are presented to show the effectiveness and advantages of the proposed method.

Adaptive Ensemble-Based Electrochemical–Thermal Degradation State Estimation of Lithium-Ion Batteries

Abstract: In this article, a computationally efficient state estimation method for lithium-ion (Li-ion) batteries is proposed based on a degradation-conscious high-fidelity electrochemical–thermal model for advanced battery management systems. The computational burden caused by the high-dimensional nonlinear nature of the battery model is effectively eased by adopting an ensemble-based state estimator using the singular evolutive interpolated Kalman filter (SEIKF). Unlike the existing schemes, it shows that the proposed algorithm intrinsically ensures mass conservation without imposing additional constraints, leading to a battery state estimator simple to tune and fast to converge. The model uncertainty caused by battery degradation and the measurement errors are properly addressed by the proposed scheme as it adaptively adjusts the error covariance matrices of the SEIKF. The performance of the proposed adaptive ensemble-based Li-ion battery state estimator is examined by comparing it with some well-established nonlinear estimation techniques that have been used previously for battery electrochemical state estimation, and the results show that excellent performance can be provided in terms of accuracy, computational speed, and robustness.

Virtual Voltage Vector Based Model Predictive Control for a Nine-Phase Open-End Winding PMSM With a Common DC Bus

Abstract: Finite control set model predictive control (FCS-MPC) is a control strategy with fast response and a simple and flexible structure. However, when the control plant is complicated such as a multiphase electric machine, the application of FCS-MPC faces clear challenges. This article for the first time investigates the FCS-MPC for a nine-phase open-end winding (OW) permanent magnet synchronous machine, which is powered by nine H-bridge inverters with a common dc bus. First, in order to solve the challenge of substantial iterations in the conventional FCS-MPC, the number of control sets is simplified by reconfiguring the high level in switching states. Then, to eliminate the zero-sequence current caused by the common dc bus, the zero common-mode voltage (CMV) vector is selected. Subsequently, duty-ratio optimization is used to further reduce the available vectors. By the abovementioned measures, the number of iterations is reduced from 19 171 to 18. In order to suppress the harmonic current, the virtual voltage vectors (VVs) are designed. Each VV is synthesized by two zero CMV vectors, which can eliminate all the third and fifth harmonics in the output voltage. In addition, to achieve symmetrical pulsewidth modulation pulse sequences, a general pulse generation method for OW drive systems is proposed. Finally, the control performance of different control sets and harmonic weighting factors are evaluated and compared, and the experimental results have verified the effectiveness of the proposed methods.

A Fast Impedance Calculation-Based Battery State-of-Health Estimation Method

Abstract: State-of-health (SOH) is crucial to the maintenance of various kinds of energy storage systems, including power batteries. Relevant research articles are mostly based on battery external information, such as current, voltage, and temperature, which are susceptible to fluctuation and ultimately affects the SOH estimation accuracy. In this article, to solve these problems, a fast impedance calculation-based battery SOH estimation method for lithium-ion battery is proposed from the perspective of electrochemical impedance spectroscopy (EIS). The relationship between EIS and state of charge and that between EIS and degraded capacity is first studied by experimental tests. Some impedance features called health factors effectively indicating battery aging states are selected. Second, an improved fast Fourier transform (FFT) utilizing the conversion relationship between the real and complex signals is proposed to realize online fast EIS acquisition. Compared with ordinary FFT, such treatments reduce computational complexity. Then, the SOH evaluation model is built by the extreme learning machine with regularization mechanism, further reducing the computational burden. The relationship between the health factors and aging capacity of batteries is established. Finally, an experimental bench is established. The results indicate that the estimated SOH can be obtained within 35 s for a four-cell series-connected battery pack and the estimation errors are less than 2%.

Dynamic Graph-Based Feature Learning With Few Edges Considering Noisy Samples for Rotating Machinery Fault Diagnosis

Abstract: Due to its ability to learn the relationship among nodes from graph data, the graph convolution network (GCN) has received extensive attention. In the machine fault diagnosis field, it needs to construct input graphs reflecting features and relationships of the monitoring signals. Thus, the quality of the input graph affects the diagnostic performance. But it still has two limitations: 1) the constructed input graph usually has redundant edges, consuming excessive computational costs; 2) the constructed input graph cannot reflect the relationship between the noisy signals well. In order to overcome them, a dynamic graph-based feature learning with few edges considering noisy samples is proposed for rotating machinery fault diagnosis in this article. Noisy vibration signals are converted into one spectrum feature-based static graph, where redundant edges are simplified by the distance metric function. Edge connections of the input static graph are updated according to the relationship among high-level features extracted by the GCN. Based on this, dynamic input graphs are reconstructed as new graph representations for noisy samples. To verify the effectiveness of the proposed method, validation experiments were conducted on practical platforms, and results show that the dynamic input graph with few edges can effectively improve the diagnostic performance under different SNRs.

Active Disturbance Rejection Control Design With Suppression of Sensor Noise Effects in Application to DC–DC Buck Power Converter

Abstract: The performance of active disturbance rejection control (ADRC) algorithms can be limited in practice by high-frequency measurement noise. In this article, this problem is addressed by transforming the high-gain extended state observer (ESO), which is the inherent element of ADRC, into a new cascade observer structure. Set of experiments, performed on a dc–dc buck power converter system, show that the new cascade ESO design, compared to the conventional approach, effectively suppresses the detrimental effect of sensor noise overamplification while increasing the estimation/control performance. The proposed design is also analyzed with a low-pass filter at the converter output, which is a common technique for reducing measurement noise in industrial applications.

Coestimation of State-of-Charge and State-of-Health for Power Batteries Based on Multithread Dynamic Optimization Method

Abstract: Accurate estimation of state-of-charge (SOC) and state-of-health (SOH) is extremely important for the state diagnosis of power batteries, which is related to the energy efficiency and safety of electric vehicles. However, in order to represent the signal noises of sensors, the most commonly used method based on Kalman filter introduces the random Gaussian noise into the estimation, which causes the uncertainty of the estimation results. In this article, the multithread dynamic optimization method is proposed to solve the problem. In addition, the fractional-order model and the unscented Kalman filter are used in SOC estimation. The Gaussian linear models based on parameters of six commonly used open-circuit-voltage models are proposed to estimate SOH. Finally, the dynamic stress test current condition and four lithium-ion batteries are implemented to verify the effectiveness of the proposed method in the experiment. For SOC estimation, root-mean-square error (RMSE) of the proposed method is 0.098 and the average value of the six models is 0.123, thus the proposed method improves the estimation accuracy by 20.32%. For SOH estimation, we compare the smallest RMSE of the six models and that of the proposed method for four experimental batteries, thus the average improvement of accuracy is 25.44%.

Generating Any Number of Initial Offset-Boosted Coexisting Chua’s Double-Scroll Attractors via Piecewise-Nonlinear Memristor

Abstract: Due to the natural nonlinearity and unique memory characteristics, memristors are promising candidates for the construction of multiscroll attractors having better application potential in the field of information encryption than the traditional double-scroll attractors. This article proposes a novel memristive multidouble-scroll Chua’s system (MMDSCS) via coupling a nonideal flux-controlled memristor with multipiecewise-linear memductance function in Chua’s system directly. Specially, any number of multidouble-scroll chaotic attractors can be generated through adjusting the internal parameters of the memristor conveniently and without changing the original system’s nonlinearity. Moreover, the amount of double scrolls is also closely related to the strength of the memristive coupling. Another striking highlight is that infinite initial offset-boosted coexisting Chua’s double-scroll attractors with the same shape are produced with the variation of the memristor initial conditions, indicating the emergence of an intriguing phenomenon of homogeneous extreme multistability. This unique property and its formation mechanism are investigated in detail using phase portraits, bifurcation diagrams, Lyapunov exponents, time series, and attraction basins. Furthermore, hardware experiments based on the field-programmable gate array are carried out to confirm the numerical simulations. Finally, an image encryption scheme is designed based on the memristor initial offset boosting dynamics from a perspective of engineering application. In comparison with the existing memristive Chua’s systems, the proposed MMDSCS has many merits, such as multidouble-scroll attractors, memristor initial-controlled chaotic sequences with controllability, good robustness, and high security performance, which is more practical in applications involving information confidential communication.

Detection of Permanent Magnet Damage of PMSM Drive Based on Direct Analysis of the Stator Phase Currents Using Convolutional Neural Network

Abstract: Permanent magnet synchronous motors (PMSMs) due to their numerous advantages, such as simple and compact design, easy production and high power-to-weight ratio, high power factor in the range of constant torque, low inertia, and more precise control compared to other electric motors, are increasingly used in various applications, such as electric vehicles, wind power, home, and industrial appliances. However, PMSMs, such as other electrical machines, may subject to various types of damage during the operation. Due to their use in devices of a critical nature (e.g., transport applications), the detection of these damages at their initial stage constitutes an extremely important issue. In this article, we investigate the possibility of the permanent magnets (PMs) faults detection of PMSM using a convolutional neural network (CNN) based on the raw stator current data. The article aims to show the possibility of detecting the PM demagnetization using the information obtained directly from the measured current signal as well as indicate the influence of simultaneous incipient interturn short circuits and the operating condition of the drive on the accuracy of the developed diagnostic system. The results of the experimental research carried out on a specially designed PMSM show the impressive capability of the developed CNN-based diagnostic system to precisely detect the initial phase of PMs damage.

Sliding Mode Direct Torque Control of SPMSMs Based on a Hybrid Wolf Optimization Algorithm

Abstract: Direct torque control has been widely used to control surface-mounted permanent magnet synchronous motors (SPMSMs). To reduce the torque ripple and improve the flux tracking accuracy of SPMSM drives, sliding mode direct torque control (SMDTC) was developed. However, its optimal performance is hardly obtained by trial and error tuning of the control parameters. Hence, a hybrid wolf optimization algorithm (HWOA) is proposed to automatically adjust the controller's parameters of SMDTC for SPMSMs in this article. This algorithm combines the grey wolf optimization algorithm and coyote optimization algorithm. A conversion probability is designed to use them simultaneously. The proposed HWOA holds the advantages of the two algorithms. It converges very fast and can avoid local optimums effectively. Furthermore, a special fitness index with penalty terms is designed to enhance flux tracking accuracy and reduce the torque ripple of SPMSM drives. The superiority of the proposed control method is verified by an experiment.