Analysis of Inter-Turn Short-Circuit Faults in Brushless DC Motors Based on Magnetic Leakage Flux and Back Propagation Neural Network

Cao, Wenping; Huang, Rongqing; Wang, Hui; Lu, Siliang; Hu, Yawei; Hu, Cungang; Huang, Xiaoyan

doi:10.1109/tec.2023.3285899

Cited by 3 publications

(1 citation statement)

References 33 publications

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“…The detection of ITSC is conventionally encompassed within insulation studies for induction motor windings, such as insulation resistance, the polarization index, direct current conductivity, capacitive impedance testing, and the dissipation factor [4]. Additionally, there are online alternatives for detection, including the analysis of partial discharges [8,9], the monitoring of axially transmitted flux linkages [10], the analysis of voltage and current harmonics [11][12][13], and the tracking of negative-sequence impedance [14,15], acoustic emissions [16,17], and Kalman filters [18], among other techniques [19,20].…”

Section: Introductionmentioning

confidence: 99%

Detection of Inter-Turn Short Circuits in Induction Motors Using the Current Space Vector and Machine Learning Classifiers

Rengifo,

Moreira,

Vaca-Urbano

et al. 2024

Energies

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Electric motors play a fundamental role in various industries, and their relevance is strengthened in the context of the energy transition. Having efficient tools and techniques to detect and diagnose faults in electrical machines is crucial, as is providing early alerts to facilitate prompt decision-making. This study proposes indicators based on the magnitude of the space vector stator current for detecting and diagnosing incipient inter-turn short circuits (ITSCs) in induction motors (IMs). The effectiveness of these indicators was evaluated using four machine learning methods previously documented in the literature: random forests (RFs), support vector machines (SVMs), the k-nearest neighbor (kNN), and feedforward and recurrent neural networks (FNNs and RNNs). This assessment was conducted using experimental data. The results were compared with indicators based on discrete wavelet transform (DWT), demonstrating the viability of the proposed approach, which opens up a way of detecting incipient ITSCs in three-phase IMs. Furthermore, utilizing features derived from the magnitude of the spatial vector led to the successful identification of the phase affected by the fault.

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Section: Introductionmentioning

confidence: 99%

Detection of Inter-Turn Short Circuits in Induction Motors Using the Current Space Vector and Machine Learning Classifiers

Rengifo,

Moreira,

Vaca-Urbano

et al. 2024

Energies

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An economical and simple strategy for overcoming the Hall sensor faults in three phase brushless DC motor drive

Verma,

Mitra,

Bhat

et al. 2024

Circuit Theory & Apps

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SummaryBrushless DC motors (BLDCs) are highly efficient and have the least maintenance requirement due to which they have replaced many conventional AC motors in various applications like electric vehicles, fans, water pumps, and home appliances. However, like all conventional motors, performance degradation of the BLDC motors can occur due to faults like Hall sensors failure, commutation switch failure, and stator‐winding failure. Among all these failures, this paper focuses on Hall sensor failures and the execution of fault tolerant control. Hall sensors produce an output after every 60° of rotation of the rotor, generating six sequences in one electrical cycle. In this study, each sequence is defined as a sector, which corresponds to the rotor position. Based only on this sector information in each electrical cycle, this paper proposes an uncomplicated detection strategy for Hall sensor fault, which is independent of motor loading and high‐performance processing unit. Along with the diagnosis, an improved faulty signal reconstruction is implemented, which bypasses the fault and continues a smooth performance of the motor. The proposed detection and reconstruction strategies have been simulated in MATLAB Simulink 2020, and the experimental analysis has been conducted to validate the proposed strategies.

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Custom Phase Space Reconstruction Image-Driven Fault Diagnosis for PMSM Under Few-Labeled Samples

Xie,

Zhang,

Luo

et al. 2024

IEEE Trans. Power Electron.

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Analysis of Inter-Turn Short-Circuit Faults in Brushless DC Motors Based on Magnetic Leakage Flux and Back Propagation Neural Network

Cited by 3 publications

References 33 publications

Detection of Inter-Turn Short Circuits in Induction Motors Using the Current Space Vector and Machine Learning Classifiers

Detection of Inter-Turn Short Circuits in Induction Motors Using the Current Space Vector and Machine Learning Classifiers

An economical and simple strategy for overcoming the Hall sensor faults in three phase brushless DC motor drive

Custom Phase Space Reconstruction Image-Driven Fault Diagnosis for PMSM Under Few-Labeled Samples

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