Haijie Mao

To address the challenges of weak early features, challenges in feature extraction, reliance on prior knowledge in traditional diagnostic methods, and the long training time, slow training convergence, and low efficiency of mainstream serial models for inter-turn short circuits (ITSC)and inter-coil short circuits(ICSC) in permanent magnet synchronous motor (PMSM) stators, this paper proposes an intelligent fault diagnosis method combining variational mode decomposition (VMD) with a BiTCN-Transformer parallel model. First, VMD decomposes three-phase current signals into multiple scales to extract intrinsic mode functions (intrinsic mode functions ,IMFs) containing fault-sensitive information. Subsequently, leveraging the complementary advantages of BiTCN in local feature extraction and Transformer in long-range dependency modeling, a parallel dual-branch architecture is designed: the BiTCN branch captures local temporal dynamic features through bidirectional dilated convolutions, while the Transformer branch captures global dependencies via a self-attention mechanism. To reduce computational complexity, a convolutional layer with stride 8 is embedded at the front end for sequence downsampling, and implicit convolutional encoding replaces fixed positional encoding. The core encoder retains only a single-layer multi-head self-attention structure, reducing parameters for model lightweighting. Finally, features from both branches are collaboratively learned in a fusion layer for multi-scale, multi-perspective fault pattern discrimination. Experimental comparisons on the Korea Advanced Institute of Science and Technology (KAIST) PMSM stator fault dataset show that the proposed parallel architecture achieves an average diagnostic accuracy of 99.42% across 15 stator fault categories, outperforming traditional serial models and redundant multi-branch models. The method requires no prior knowledge or massive data, exhibits fast training and convergence, and demonstrates significant potential for practical engineering applications, providing an effective solution for early weak fault diagnosis in motors.

Ensuring the safety and reliability of the stage boom synchronization system is of utmost importance. The implementation of predictive and health management techniques for maintenance purposes is crucial. Acknowledging the limitations of traditional Wiener process models in accurately capturing how diverse operating conditions affect the degradation process, this paper proposes an improved nonlinear Wiener degradation modeling method that considers time-varying operating conditions. To effectively characterize the degradation state of the stage boom actuating motor in the context of varying loads and operating conditions across different performance scenes, Multiple variables reflecting the actuator's degradation are fused. The weights for each variable are determined using an enhanced affiliation function, allowing the establishment of a composite degradation quantity that accurately corresponds to the operational condition of the stage boom control system and aligns with the degradation characteristics of the actuator. By defining the first reach time, an analytical solution for the residual life probability density function is derived. Finally, through simulation experiments, the proposed method's superior accuracy in predicting the remaining life is empirically demonstrated.

This paper presents an active fault-tolerant method to mitigate sensor failures in multimotor synchronous control. First, inspired by the construction of the coupling matrix in complex network synchronous output, a consistent matrix is designed based on structural redundancy in synchronous control. This consistent matrix has two advantages: one is that it can reflect different sensor output similarities and the other one is that it can detect, locate, and estimate the sensor fault. Then, the fault information is integrated into the design of tolerance control with an improved mean feedback mechanism. The proposed method is suitable for both single and multiple fault situations, and its effectiveness is finally verified by both MATLAB simulation and the ABB semiphysical experimental platform.

In view of the speed sensor faults in elevating stage control system, an active fault tolerant control approach based on a novel tracking differentiator is proposed in this paper. First, the analytical redundancy relationship between the velocity and displacement signals in a dual closed loop control system is used to detect a fault. When the deviation between the differential of normal displacement sensor signal and the fault speed sensor output exceeds a certain threshold value, a fault can be considered to occur. Secondly, after a fault is detected, the output of the fault sensor is replaced immediately with the differential signal of the output from a normal sensor to ensure the safety of the postfault system. In the process of signal differential, considering the drawbacks of the traditional method which uses inertial element to approximate differentiator and complex parameter tuning of Han’s Tracking Differentiator (TD), a novel tracking differentiator based on hyperbolic tangent function (Tanh-TD) is designed. Thirdly, in order to avoid the switching vibration and improve the reliability of FDD, a continuous smooth switching tactic based on exponential function is constructed. The simulation results show that the fault diagnosis method is simple and timely, the designed tracking differentiator is fast and effective, and the effect of the fault tolerant control based on smooth switching strategy is also satisfactory.