Tackling the challenge of open-switch (OS) fault diagnostics in grid-tied three-level neutral point clamped (NPC) inverters with parameter uncertainty, this paper introduces a fault diagnosis method that integrates a variable forgetting factor bias-compensation recursive least squares (VFFBCRLS) algorithm with a novel discrete disturbance sliding mode observer (DSMO) for three-level inverters. The proposed approach initially employs a VFFBCRlS algorithm to obtain the uncertain parameters of the inverter. Building upon this foundation, a novel discrete DSMO is introduced to obtain the output currents rapidly and accurately. Then, an adaptive fault detection variable is constructed based on the norm of the residual between the measured and the estimated currents, ensuring the accuracy and robustness of the detection algorithm. Finally, a precise identification of OS faults in grid-tied inverters is achieved through the establishment of a localization mechanism. The hardware-in-the-loop (HIL) test results provide validation for the efficacy and robustness of the proposed method.

This article introduces an innovative approach to simultaneously estimate time-domain and spatiotemporal faults in nonlinear distributed parameter systems (NDPSs)nonlinear distributed parameter systems (NDPSs) under external disturbances. First, the establishment of an iterative learning observer that accounts for both temporal and spatial changes is presented. Next, a fault estimation law is devised utilizing a distinct full iterative learning (FIL)full iterative learning (FIL) technique, facilitating rapid and precise estimation of fault signals while mitigating the impact of external disturbances. Furthermore, the adoption of the $\lambda $ -norm method aids in simplifying the determination of convergence conditions and gain matrix calculations. Lastly, comprehensive simulation results validate the efficacy of the developed approach, underscoring its adeptness in efficiently and precisely estimating faults across both time and spatiotemporal domains.

State of health (SOH) estimation is essential for battery health monitoring, particularly in cross-domain scenarios where data variability and domain shifts present significant challenges. To address these issues, this study proposes the adversarial dynamic spatiotemporal attention network (ADSTAN), which integrates a graph attention network for spatial feature extraction, a gated recurrent unit for temporal dependency modeling, and a gradient reversal layer-based domain alignment module for unsupervised domain adaptation. Representing battery health data as dynamic graphs, with each cycle serving as a node, ADSTAN effectively captures spatiotemporal dependencies and dynamically aligns feature distributions between source and target domains. Experiments on cross-domain datasets, including the CALCE and NASA battery datasets, demonstrate the model’s effectiveness. Using data from 10 cycles to predict SOH for 5, 10, and 15 horizons, ADSTAN achieved RMSE values of 2.49%, 2.61%, and 2.94%, respectively. Ablation experiments validated the model’s design, highlighting the superiority of its spatial, temporal, and alignment modules. These results underscore ADSTAN’s robust performance and its suitability for accurate and generalizable SOH estimation in diverse cross-domain settings.

In this article, a distributed fault estimation (DFE) approach for switched interconnected nonlinear systems (SINSs) with time delays and external disturbances is proposed using a novel segmented iterative learning scheme (SILS). First, through the utilization of interrelated information among subsystems, a distributed iterative learning observer is developed to enhance the accuracy of fault estimation results, which can realize the fault estimation of all subsystems under time delays and external disturbances. Simultaneously, to facilitate rapid fault information tracking and significantly reduce sensitivity to interference, a new SILS-based fault estimation law is constructed by combining the idea of segmented design with the method of variable gain. Then, an assessment of the convergence of the established fault estimation methodology is conducted, and the configurations of observer gain matrices and iterative learning gain matrices are duly accomplished. Finally, simulation results are showcased to demonstrate the superiority and feasibility of the developed fault estimation approach.

Accurate fault detection and localization are essential for single-phase three-level (SPTL) rectifier systems with high reliability requirements. However, power switch faults, grid-side current sensor (CS) faults, and DC-link voltage sensor (VS) faults can all contribute to distorted output in the rectifier system, posing challenges for existing diagnostic methods tailored for single-type faults, as they struggle to distinguish between these various faults. Therefore, this study proposes a comprehensive diagnosis technology for open-circuit (OC) faults, CS faults, and VS faults of SPTL rectifiers on the basis of a reduced-order observer. To achieve this, the method begins by expanding and transforming the state equation of the rectifier with faults, ensuring complete decoupling of the OC fault vector from the initial system states and sensor faults. Subsequently, an assessment of the initial system state, CS faults, and VS faults is achieved via the design of a reduced-order observer. Using these estimation results, fault detection variable and its adaptive thresholds is designed, along with fault-distinguishing variables to differentiate between sensor faults and OC faults. Simultaneously, sensor fault identification method and OC fault location method are introduced. Finally, the validity and resilience of the comprehensive diagnostic approach are confirmed through hardware-in-the-loop (HIL) test results under diverse scenarios.

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 Metropolis–Hastings (M-H) sampling method is used to estimate the unknown parameters used in the proposed model. For illustration, the electrical connector 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.

Features of incipient faults are tiny in high-speed trains’ electrical drive systems. Noises and disturbances in the external environment and sensors can mask incipient faults. Therefore, fault detection (FD) of incipient faults is a challenge. This paper proposes a new FD scheme using a novel manifold learning method named local linear generalized autoencoder (LLGAE). The prominent characteristics of the LLGAE-based FD method are three-fold: 1) it can realize FD for electric drive systems even without the physical model or expertise; 2) it still has good results for non-Gaussian electrical drives; 3) it entirely takes into account the locally linear structure of samples. Mathematical derivations have proved the proposed method. Through an experimental platform of high-speed trains, the proposed method is validated.