Self-powered synchronized switching harvesting on an inductor (SSHI) circuits have been proved to greatly increase the performance of a PVEH device and peak detector-based self-powered switches are widely used. In practice, however, the switch is impossible to turn on simultaneously at peak displacements due to nonlinear components, so that switching delay(SD) always exists. Furthermore, the SD will degrade the performance of PVEH devices, so it must be reduced. Therefore, for this kind of SSHI circuits, the purpose of this paper is to explore basic causes of generating SD and investigate the corresponding solution. Firstly, theoretical model of SD in self-powered parallel (SP-PSSHI) is derived and the SD is first proved to be positive. Then effects of key component parameters on the SD are studied. Based on above results, an improved SP-PSSHI (ISP-PSSHI) circuit is proposed by adding a voltage divider and its SD is proved to be less than that of the SP-PSSHI circuit. Circuit simulations validate theoretical results and also expose that there are optimal resistor and capacitor of the envelope detector for achieving the maximum harvested power. In the end, experimental results show that the ISP-PSSHI circuit can improve the averaged harvested power about 11 percent more than that of the SP-PSSHI circuit under choosing optimal components.

A demagnetization fault not only degrades the performance of a permanent magnet synchronous motor (PMSM) drive system, but also may even cause the irreversible demagnetization. This article presents an improved-sliding-mode-observer (ISMO) based equivalent-input-disturbance (EID) method to reconstruct a demagnetization fault and to perform fault-tolerant control for the demagnetization fault in a PMSM drive system. The models of healthy and demagnetized PMSM are first established. Next, an ISMO is constructed by inserting a decoupling term. It completely removes the influence of the motor speed on the reconstruction of a demagnetization fault. Then, the ISMO-based EID approach is developed to reconstruct the fault with the high precision and maintain system control performance even for a demagnetization fault. The stability of the whole system is proved by using the concept of globally uniformly ultimately bounded. Finally, the validity of the method is demonstrated using hardware-in-the-loop simulation.

This paper proposes a new scheme of reconstructing current sensor faults and estimating unknown load disturbance for a permanent magnet synchronous motor (PMSM)-driven system. First, the original PMSM system is transformed into two subsystems; the first subsystem has unknown system load disturbances, which are unrelated to sensor faults, and the second subsystem has sensor faults, but is free from unknown load disturbances. Introducing a new state variable, the augmented subsystem that has sensor faults can be transformed into having actuator faults. Second, two sliding mode observers (SMOs) are designed: the unknown load disturbance is estimated by the first SMO in the subsystem, which has unknown load disturbance, and the sensor faults can be reconstructed using the second SMO in the augmented subsystem, which has sensor faults. The gains of the proposed SMOs and their stability analysis are developed via the solution of linear matrix inequality (LMI). Finally, the effectiveness of the proposed scheme was verified by simulations and experiments. The results demonstrate that the proposed scheme can reconstruct current sensor faults and estimate unknown load disturbance for the PMSM-driven system.

The model is difficult to establish because the principle of the locomotive adhesion process is complex. This paper presents a data-driven adhesion status fault diagnosis method based on deep learning theory. The adhesion coefficient and creep speed of a locomotive constitute the characteristic vector. The sparse autoencoder unsupervised learning network studies the input vector, and the single-layer network is superimposed to form a deep neural network. Finally, a small amount of labeled data is used to fine-tune training the entire deep neural network, and the locomotive adhesion state fault diagnosis model is established. Experimental results show that the proposed method can achieve a 99.3% locomotive adhesion state diagnosis accuracy and satisfy actual engineering monitoring requirements.

This study presents a novel observer‐based electronic line‐shafting control strategy for permanent magnet synchronous motor systems. Adopting the sliding‐mode variable structure technology, the proportional–integral speed controller for every servo unit is designed, followed by design of two cascades connected observers, in order to improve the system synchronisation performance when a large load disturbance occurs. The load torque observer transmits the observed load torque value instead of the calculated one back to the virtual shafting, which reflects the dynamic relationships of all the virtual or slave shafts more accurately. Another load torque derivative observer feeds the observed value forward to the corresponding sliding‐mode controller for reducing the switching gain. The proposed control system is proved stable by Lyapunov stability theory, and simulation results show the strategy is effective on reducing sliding‐mode chattering and achieving higher synchronisation precision.

To prevent irreversible demagnetization of a permanent magnet (PM) for interior permanent magnet synchronous motors (IPMSMs) by flux-weakening control, a robust PM flux-linkage nonsingular fast terminal-sliding-mode observer (NFTSMO) is proposed to detect demagnetization faults. First, the IPMSM mathematical model of demagnetization is presented. Second, the construction of the NFTSMO to estimate PM demagnetization faults in IPMSM is described, and a proof of observer stability is given. The fault decision criteria and fault-processing method are also presented. Finally, the proposed scheme was simulated using MATLAB/Simulink and implemented on the RT-LABplatform. A number of robustness tests have been carried out. The scheme shows good performance in spite of speed fluctuations, torque ripples and the uncertainties of stator resistance.