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.

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.

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.