A traction drive system (TDS) in high-speed trains is composed of various modules including rectifier, intermediate dc link, inverter, and others; the sensor fault of one module will lead to abnormal measurement of sensor in other modules. At the same time, the fault diagnosis methods based on single-operating condition are unsuitable to the TDS under multi-operating conditions, because a fault appears various in different conditions. To this end, a real-time causality representation learning based on just-in-time learning (JITL) and modular Bayesian network (MBN) is proposed to diagnose its sensor faults. In specific, the proposed method tracks the change of operating conditions and learns potential features in real time by JITL. Then, the MBN learns causality representation between faults and features to diagnose sensor faults. Due to the reduction of the nodes number, the MBN alleviates the problem of slow real-time modeling speed. To verity the effectiveness of the proposed method, experiments are carried out. The results show that the proposed method has the best performance than several traditional methods in the term of fault diagnosis accuracy.

Due to the non-intuitive high-level semantics of fault samples, such as the fault type and operating condition, traditional unsupervised methods are not suitable for fault data augmentation in traction motors. To this end, a new method termed disentangled semantic embedding in variational autoencoder(DSE-VAE) is proposed to learn the interpretable representation of fault samples. In DSE-VAE, a regularization term is constructed by introducing attribute labels to bridge the latent space and semantics. In addition, the mutual information between different attributes is minimized to achieve attribute disentanglement. Extensive experiments are conducted on the hardware-in-the-loop (HIL)real-time platform. Experimental results suggest that the proposed DSE-VAE could learn an interpretable fault sample generation.

This paper proposes an energy control strategy based on adaptive fuzzy logic for onboard hybrid energy storage system (HESS) with lithium-ion batteries (LIB) and electric double-layer capacitors (EDLC). Firstly, adaptive fuzzy logic energy control method for the system is proposed. The fuzzy rules are modified and reorganized according to the system deviation and deviation change rate to improve the energy-saving and voltage-stabilizing effect. Secondly, “soft connection out” and dead zone control method are implemented in closed-loop control to improve system stability and reduces voltage oscillation. Finally, the proposed strategy is compared with the classical strategy through simulation, where it shows better performance in the increase of 0.62% in voltage stabilization rate and 0.59% in energy conservation rate.

A LADRC-based control strategy with performance guarantee for on-board supercapacitor (SC) energy storage systems (ESS) of urban rail traction is proposed in this paper. An outer loop control structure utilizing linear active disturbance rejection control (LADRC) is employed to improve bus voltage tracking performance and alleviate the impact of system disturbances and minor faults that may cause performance degradation. A fuzzy logic-based state of charge (SOC) restriction unit is implemented to prevent overcharging and over-discharging of the SC. Moreover, a hysteresis-comparison-based selecting signal generator is used for switching between charge and discharge modes. Simulation results show that the proposed strategy outperforms the traditional double closed-loop PI control in terms of control performance and provides better guaranteed performance.

Intelligent fault diagnosis of the complex systems has made great progress based on the availability of massive labeled data. However, due to the diversity of working conditions and the lack of sufficient fault samples in practice, the generalization of the existing fault diagnosis methods are weak. To handle this issue, a multichannel domain adaptation graph convolutional network method is proposed. In the proposed network, a feature mapping layer based on convolutional neural network is used first to extract features from input data, which then are transmitted to the graph generator to construct two association graphs. After that, three distributed graph convolutional networks are used to extract the specific and common embeddings from two association graphs and their combination. Meanwhile, to fuse these embeddings adaptively, an attention mechanism is used to learn importance weights. Besides, a domain discriminator is leveraged to reduce the distribution discrepancy of different data domains. Finally, a label classifier is used to output fault diagnosis results. Two experimental studies with different signal types show that the proposed method not only presents better diagnosis performance than existing methods with few samples, but also can extract domain-invariant features for cross-domain under varying working conditions.

In the conventional graph convolutional network (GCN)-based fault diagnosis method, multilayer GCN model is often used for feature extraction. However, the application of multilayer GCN will encounter oversmoothing problem, and thus reduce the diagnostic performance. Therefore, the oversmoothing relief GCN (OsR-GCN) method is proposed. Specifically, two association graph construction methods, namely the Euclidean distance (ED)-based method and the structure analysis (SA)-based method, are first introduced. Then, the constructed graph and measurements are input to the OsR-GCN model, in which a weight coefficient is proposed to relieve the oversmoothing problem. Next, an improved particle swarm optimization algorithm is introduced to find the optimal weight coefficient. Finally, the proposed method is applied to diagnose the pulse rectifier faults in a hardware-in-the-loop simulated traction control system of high-speed trains. The achieved results show that the proposed method outperforms the existing fault diagnosis methods.

Deep-neural network-based fault diagnosis methods have been widely used according to the state of the art. However, a few of them consider the prior knowledge of the system of interest, which is beneficial for fault diagnosis. To this end, a new fault diagnosis method based on the graph convolutional network (GCN) using a hybrid of the available measurement and the prior knowledge is proposed. Specifically, this method first uses the structural analysis (SA) method to prediagnose the fault and then converts the prediagnosis results into the association graph. Then, the graph and measurements are sent into the GCN model, in which a weight coefficient is introduced to adjust the influence of measurements and the prior knowledge. In this method, the graph structure of GCN is used as a joint point to connect SA based on the model and GCN based on data. In order to verify the effectiveness of the proposed method, an experiment is carried out. The results show that the proposed method, which combines the advantages of both SA and GCN, has better diagnosis results than the existing methods based on common evaluation indicators.

Multivariate analysis is an important kind of method in process monitoring and fault detection, in which the canonical correlation analysis (CCA) makes use of the correlation change between two groups of variables to distinguish the system status and has been greatly studied and applied. For the monitoring of nonlinear dynamic systems, the deep neural network-aided CCA (DNN-CCA) has received much attention recently, but it lacks a general definition and comparative study of different network structures. Therefore, this article first introduces four deep neural network (DNN) models that are suitable to combine with CCA, and the general form of DNN-CCA is given in detail. Then, the experimental comparison of these methods is conducted through three cases, so as to analyze the characteristics and distinctions of CCA aided by each DNN model. Finally, some suggestions on method selection are summarized, and the existed open issues in the current DNN-CCA form and future directions are discussed.

This article proposes a new data-driven health monitoring method, which uses multiobjective optimization and stacked autoencoder based health indicator. Specifically, the proposed method proposes an improved nondominated sorting genetic algorithm-II (NSGA-II) to perform multiobjective optimization on a large number of candidate features extracted from the sensor measurements. Then, a stacked autoencoder model is used to construct health indicators from the selected features. In the improved NSGA-II algorithm, the optimization goals of feature selection are defined as the minimum gap of health indicators between different states and the number of features. Comparisons between the proposed method and the state-of-the-art methods on simulation experiments show that the proposed method can accurately identify the status of the equipment and effectively limit the complexity of the diagnostic model.

Demagnetization, as one of the most frequent faults, has great influence on the performance of (permanent magnet synchronous motor) PMSM. However, the motor usually runs in nonstationary conditions, that brings great challenge to the effective diagnosis of demagnetization fault. This paper presents a new methodology of Shift-invariant Dictionary of Dynamic Time Warping (SIDDTW) to diagnose the demagnetization fault under nonstationary conditions. Firstly, according to the characteristics of current signal under demagnetization fault, the shift-invariant dictionary is constructed. Then, Matching Pursuit (MP) is used to represent the current signals that collected from the running process of PMSM, and then the sparse coefficient series are obtained. Finally, the Dynamic Time Warping (DTW) method is used to calculate the sparse coefficient series distance between the test data and the database which build in the training process. In this step, the nearest distance is matched, and corresponding operation state is recognized as the final diagnosis result. The results show that the presented method has good adaptability when dealing with nonstationary conditions both on the Simulink platform and the real-time simulation platform.

In this paper, a new data-driven fault detection method based on distributed canonical correlation analysis (D-CCA) is proposed to address the plant-wide process monitoring problem. This paper focuses on the distributed plant-wide processes. The core of the proposed method is to reduce uncertainties using correlation information from the neighboring nodes. Furthermore, the cost of the data transmission between network nodes is also reduced by the D-CCA algorithm. When the proposed method and the existing methods are compared using the Tennessee Eastman benchmark process, the false alarm rate, fault detection rate, and the detection delay are comparable. This suggests that the proposed method is feasible.

This paper presents an adaptive data-driven fault detection method for dynamic processes. In this method, the vector ARX model is used to model the dynamic process in a data-driven fashion. Then, the adaptive method is developed by means of the incremental and decremental algorithms. The performance and effectiveness of the proposed approach are demonstrated with a numerical case study and an experimental continuous stirred tank heater. The detection results show that the effectiveness of the proposed method.

The problem of fault diagnosis for static processes has been well studied over the last decades. However, fault diagnosis methods have rarely considered processes subject to unknown periodic disturbances. Using the well-established orthogonal function technique, this paper proposes a data-driven fault diagnosis method to deal with this challenge. The basic idea is to first design the orthogonal functions, and then identify the unknown weighting parameters. By removing the influence of the periodic disturbances, the residual signal can be obtained. Then, the fault detection problem is solved by monitoring the change of the residual signal. The performance and effectiveness of the proposed approach are demonstrated with a numerical case study and an experimental study based on a pilot scale, continuous stirred tank heater.

The interleaved bidirectional DC-DC converter is a core component of the battery energy storage system. However, if an open-switch fault occurs at the interleaved bidirectional DC-DC converters, the currents of two branches will become unbalanced, which will increase the battery current ripple and shorten the life of batteries and energy storage systems. In this paper, a fault tolerant topology is proposed to improve the reliability of the interleaved parallel bidirectional DC/DC converter. Firstly, the normal operation of the battery energy storage system has been analysed. Then, the fault-tolerant topology is presented. Compared with the normal topology, the fault tolerant topology adds an additional branch of bidirectional switch. When an open-switch fault occurs, the faulty branch would be cut off, then, the fault-tolerant circuit will be put into operation quickly, and the converter can keep continuous operation with acceptable performance. Simulation results are presented to verify the feasibility and effectiveness of the proposed topology.