A simultaneous diagnosis method for power switch open-circuit faults and current sensor faults of grid-connected three-level neutral point clamped (NPC) inverters is proposed herein. First, by designing the adaptive reaching law and reducing the steady-state resonance, a novel interval sliding model observer is developed to track and estimate the three-phase currents accurately and rapidly. Next, a faulty phase detection scheme using an adaptive threshold is proposed to achieve sensitive and robust detection results. Also, the measured three-phase currents sum and estimated three-phase currents sum are used to distinguish power switch open-circuit faults and current sensor faults. Subsequently, a faulty switch location method and the current sensor fault type identification method are proposed, using some of the same identification variables. Finally, combining the faulty phase detection variables with the identification methods ensures the feasibility of the detection and identification of 12 open-circuit faults and 9 current sensor faults. The hardware-in-the-loop test results are performed to validate the effectiveness and robustness of the proposed method.

Fault diagnosis and identification play a crucial role in ensuring the high reliability of permanent magnet synchronous motor (PMSM) drive systems used in electric vehicles. However, detecting and differentiating insulated gate bipolar transistor (IGBT) open-circuit (OC) faults and current sensor (CS) incipient faults in PMSM drive inverters pose challenges due to their similar effects on phase current distortion. To address this issue, this paper proposes a novel diagnosis strategy that enables simultaneous detection of IGBT OC faults and CS incipient faults in inverters of PMSM drives. Firstly, a comprehensive inverter system model is built that incorporates IGBT OC faults and CS incipient faults. Next, to distinguish between these two fault types, the comprehensive inverter system undergoes augmentation and nonsingular coordinate transformation, effectively decoupling it into two subsystems: one exclusively containing IGBT OC faults and the other solely dedicated to CS incipient faults. Subsequently, adaptive sliding mode observers (SMOs) are constructed to accurately estimate the reconfiguration state of each subsystem. Finally, by using observer residuals, fault diagnosis algorithms with adaptive thresholds are proposed to locate defective IGBTs and detail the process of CS from incipient fault to fault or even failure. Experimental results and comparisons validate the rapidity, robustness, and effectiveness of the proposed diagnostic algorithm for simultaneously diagnosing IGBT OC and CS incipient faults.

This article presents a reduced-order observer-based simultaneous diagnosis strategy for grid-tied neutral point clamped (NPC) inverters subjected to open-switch and current sensor faults. First, the augmented descriptor system is constructed for the NPC inverter to transfer the current sensor fault into a generalized state vector. Then, the matrix transformations are applied to decouple the open-switch fault from the inverter system state and the current sensor fault. Subsequently, a reduced-order observer is developed for the transformed augmented descriptor system to achieve a simultaneous precise estimation of the phase current and the current sensor fault. Finally, using the estimation results, a diagnosis algorithm with an adaptive threshold is proposed, which can not only distinguish between current sensor faults and open-switch faults but also locate the faulty power switch and estimate different types of current sensor faults. Experimental results and comparisons are provided to verify the robustness and effectiveness of the proposed fault diagnosis algorithm.

The focus of this paper is a fault-estimation issue for switched interconnected nonlinear systems (SINSs) subjected to external disturbances. First, to estimate the fault of all subsystems under external disturbances, a distributed iterative learning observer (DILO) is designed by utilizing related information among subsystems. Then, a novel variable weighted iterative learning (VWIL)-based fault-estimation law is proposed to fast-track the fault signals and weaken the disturbance effects. Subsequently, the convergence conditions are achieved using λ-norm method and mathematical induction. In addition, the gain matrices of the DILO and VWIL law are calculated simultaneously. Finally, simulation results are given to verify the feasibility of the proposed method, which show that accurate fault-estimation results can be obtained after about the 20th iteration.

In order to achieve fault diagnosis and prognosis, one needs a sufficient and valid life-cycle data. However, this requirement is difficult for current high-reliable manufacturing system. Good thing is that the technique of accelerated degradation testing can be used to address this issue. Bad thing is that, it needs a reliable testing/measuring technique to build an accurate model for accelerated degradation testing. However, in practical applications, data acquisition is obtained by sensors or measurement devices, which cannot guarantee perfect working condition under the influence of external environment and stresses, resulting in unreliable signals. Furthermore, since traditional models require complex differentiation and cannot obtain analytical expressions when considering unreliable signals, traditional models rarely reflect well this situation. Motivated by these facts, an accurate model for the accelerated degradation testing is proposed in this study with considering the unreliable signals. Based on the proposed model, a closed-form expression for the useful life analysis is derived. The M-H sampling method is used to estimate the unknown parameters used in the proposed model. For illustration, the electrical connectors dataset is analyzed with the proposed model and the traditional models. Comparing the obtained results, the proposed model is more accurate in the useful life analysis than the traditional accelerated degradation testing models by considering the unreliable signals.

This paper develops a novel sliding mode control technology and a comprehensive evaluation methodology for speed control of permanent magnet synchronous motor (PMSM). In most of the existing literature, only control precision of the speed corrector is investigated. However, the control strategy should be stable to the noise and disturbance caused by the environment and parameter uncertainties. In order to possessing faster dynamic response, stronger anti-interference ability and high stability of input, a highly stable sliding surface is proposed for siding mode control. Unlike most of reaching laws that requires the variation of designed sliding surfaces, this method takes state vectors in consideration. In addition, a comprehensive evaluation algorithm is presented to obtain reasonable assessment on speed control for synchronous PMSMs in different operating phases (starting, transient, steady-state rating). Finally, the simulation results verify the effectiveness and validity of the proposed sliding mode speed controller.