Fault Diagnosis Method Based on Temperature Rise Detection for Switched Reluctance Motor Drive Systems in Electrical Transportation

Fault detection and classification of electric vehicle motor drive using ensemble subspace k-nearest neighbour model

An ensemble subspace k-nearest neighbour model for fault detection and classification of switch open-circuit and short-circuit faults and indicates that the proposed model provides an effective and computationally efficient solution for reliable fault diagnosis in electric vehicle motor drive systems.

Fault Diagnosis of EV SRM Multilevel Converters Based on a Novel Current Reconstruction Method

Stator Fault Classifier in a Doubly-Fed Induction Genera-tor DFIG

Fault detection is one of the most common studies on wind turbines. In this case, the doubly fed induction generator (DFIG) is specifically analyzed. The fault cases analyzed are: inter-turn short circuit and open circuit, in addition to normal operating conditions. The K-means algorithm was used for analysis and classification. The data set is obtained from multiple simulations in MATLAB/Simulink, in which the stator resistance (Rs) and stator inductance (Ls) were varied. From these simulations, the current and voltage signals are processed using tools such as the Park transform, stator current imbalance, and harmonic analysis to obtain relevant characteristics for the classification of each case. It is known that Rs is a determining parameter in fault detection: in a short circuit, Rs tends to fall below its nominal value due to the appearance of a low-impedance path, while in an open circuit, Rs tends to rise above the nominal value due to the interruption of the conductor. The results obtained indicate that the K-means algorithm, together with the proposed methodology, are efficient in classifying the different stator states. This suggests that the proposed solution, based on the results, could be effective and economical for wind turbine monitoring.

Phase Voltage-Based Diagnosis of Inter-Turn Short Circuits in Permanent Magnet Synchronous Motor Stator Windings

The problem of fault diagnosis in electrical motors has an important impact on the supervision of dynamic systems, and model-based methods are efficient tools for this purpose. In this regard, this work presents a novel method for detecting inter-turn short circuits (ITSCs) in the stator windings of permanent magnet synchronous motors (PMSMs). The approach is based on algebraic identification to process the motor voltage signals, estimating the offsets, amplitudes, and phases of the fundamental and third-harmonic components. Fault detection is performed in two steps: first, a voltage imbalance index is evaluated to determine the presence of abnormal operating conditions. Subsequently, characteristic patterns in the estimated parameters are analyzed to identify both the fault type and the affected phase(s). The experimental results show that single-phase ITSC faults produce a reduction in the offset of the faulted phase together with an increase in its third-harmonic amplitude, whereas phase-to-phase ITSC faults lead to an increase in the offsets of the affected phases and nearly identical third-harmonic amplitudes between them. In both cases, only minor variations are observed in the estimated phase angles. The effectiveness of the proposed methodology is supported through theoretical analysis and validated experimentally using voltage measurements acquired from a PMSM test bench. The results demonstrate that the proposed technique can accurately identify fault conditions through voltage imbalance and harmonic-pattern analysis, providing a practical and computationally efficient methodology for PMSM stator winding fault diagnosis.

Research on Rectifier Fault Diagnosis Method in Wireless Power Transfer Systems Based on Harmonic Analysis

Inductive power transfer (IPT) technology has become a research focus due to its convenience, flexibility, and environmental adaptability. However, the high failure rate of power electronic devices in IPT systems poses a significant challenge, with the precise diagnosis and localization of rectifier faults being a critical issue that requires immediate attention. This paper proposes a fault diagnosis method based on harmonic analysis for rectifier short‐circuit faults in IPT systems. Initially, simulations were conducted to analyze the electrical characteristics of faults at different locations and the potential secondary faults they might cause. Subsequently, a mathematical model of the system was established, and the time‐domain expressions of fault characteristics were derived, with their analytical expressions in the frequency domain obtained through Fourier transformation. Based on this, by analyzing the mapping relationship between different fault types and frequency‐domain characteristics, a diagnostic scheme capable of fault type identification and localization was constructed. Finally, an experimental platform was set up to verify the variation patterns of characteristics under single‐diode and dual‐diode short‐circuit faults.

Quantitative diagnosis of internal short circuits in lithium-ion batteries based on terminal voltage during the charging stage

Internal short-circuit (ISC) faults in lithium-ion batteries shorten service life and may cause severe safety issues such as thermal runaway. Therefore, this study proposes a purely data-driven method based on terminal voltage during charging. The analysis focuses on the stable mid-to-late stage of low-rate constant-current charging. Multiple discrete time instants within 4200 s–6000 s are selected, and the voltage difference between them is used as the diagnostic feature. A random forest (RF) model is developed, trained, and validated. To improve performance, Bayesian optimization tunes RF hyperparameters, and particle swarm optimization is applied for feature selection. Diagnostic thresholds are established based on model outputs to enable ISC fault identification and prediction. Support vector regression and Gaussian process regression are used for comparison. Results show that the optimized RF model achieves a mean relative error of 4.254%, a root mean square error of 32.688 Ω, and a coefficient of determination (R2) of 98.42%, significantly outperforming the comparison methods.