Detection and Localization of Interturn Short-Circuit Fault by Analysis of Stator Accelerations Spectrum in Five-Phase Flux Switching Machine for HEV Application

Lanciotti, Noemi; Ojeda, Javier; Gabsi, Mohamed; Boukhobza, Taha

doi:10.1109/ever48776.2020.9243062

Cited by 2 publications

(2 citation statements)

References 24 publications

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“…Although fault analysis and condition monitoring of the FSMs have received less attention than other EMs, certain studies have concentrated on them. The most well-known study on the FSM faults analysis, according to the authors' knowledge, is presented in [2][3][4][5][6][7][8][9][10]. In [2], static eccentricity is detected by induced voltages of the open-circuit phase windings.…”

Section: Introductionmentioning

confidence: 99%

“…The same studies with comparable results are carried out on the influence of dynamic [3] and mixed [4] eccentricities on the performance of FSMs. In [5], inter-turn fault influence on a five-phase FSM is studied by the motor acceleration analysis, but the other faults are not expressed. The dynamic eccentricity of a six-phase FSM is analyzed analytically in [6], but the technique for fault detection needs to be described.…”

Section: Introductionmentioning

confidence: 99%

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An Innovative Modeling Approach and Advanced Technique for Fault Detection and Distinction in Permanent Magnet Flux-Switching Motors

Naderi,

Mikaeeli,

Ehsan-Maleki

et al. 2024

Preprint

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This paper presents a Permanent Magnet Flux Switching Motor (PM-FSM) performance analysis in healthy and faulty cases by a mathematical modeling method. A new fault detection and separation technique is described using six search windings (SWs), each obtained by two series search coils (SCs). The goal of the paper is to consider Static, dynamic, and mixed eccentricities (SE, DE, and ME), inter-turn (IT), and PM demagnetization (PD) faults. The style of the induced voltages is employed for the described fault detection/separation, where the IT and PD faults location is possible by using six open-circuit voltage of the search windings. Since each fault has a particular waveform style in the induced voltages of the SWs, the proposed method yields a real-time fast technique. The method is independent of the motor load thanks to the voltage-based approach, where the rotor rotation is the only necessity of the proposed method. Although the method used twelve SCs in the motor structure, distinguishing the various faults and their location possibility are its advantages compared to other SC-based techniques that use fewer SCs. The well-known flexible Magnetic Equivalent Circuit (MEC) with adjustable accuracy is used to analyze healthy and faulty motors by a unique model thanks to its flexibility and shorter processing time compared to the Finite-Element Method (FEM).

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Section: Introductionmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

An Innovative Modeling Approach and Advanced Technique for Fault Detection and Distinction in Permanent Magnet Flux-Switching Motors

Naderi,

Mikaeeli,

Ehsan-Maleki

et al. 2024

Preprint

View full text Add to dashboard Cite

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Intelligent Fault Diagnosis of Inter-Turn Short Circuit Faults in PMSMs for Agricultural Machinery Based on Data Fusion and Bayesian Optimization

Wang,

Lai,

Zhang

et al. 2024

Agriculture

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The permanent magnet synchronous motor (PMSM) plays an important role in the power system of agricultural machinery. Inter-turn short circuit (ITSC) faults are among the most common failures in PMSMs, and early diagnosis of these faults is crucial for enhancing the safety and reliability of motor operation. In this article, a multi-source data-fusion algorithm based on convolutional neural networks (CNNs) has been proposed for the early fault diagnosis of ITSCs. The contributions of this paper can be summarized in three main aspects. Firstly, synchronizing data from different signals extracted by different devices presents a significant challenge. To address this, a signal synchronization method based on maximum cross-correlation is proposed to construct a synchronized dataset of current and vibration signals. Secondly, applying a traditional CNN to the data fusion of different signals is challenging. To solve this problem, a multi-stream high-level feature fusion algorithm based on a channel attention mechanism is proposed. Thirdly, to tackle the issue of hyperparameter tuning in deep learning models, a hyperparameter optimization method based on Bayesian optimization is proposed. Experiments are conducted based on the derived early-stage ITSC fault-severity indicator, validating the effectiveness of the proposed fault-diagnosis algorithm.

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Detection and Localization of Interturn Short-Circuit Fault by Analysis of Stator Accelerations Spectrum in Five-Phase Flux Switching Machine for HEV Application

Cited by 2 publications

References 24 publications

An Innovative Modeling Approach and Advanced Technique for Fault Detection and Distinction in Permanent Magnet Flux-Switching Motors

An Innovative Modeling Approach and Advanced Technique for Fault Detection and Distinction in Permanent Magnet Flux-Switching Motors

Intelligent Fault Diagnosis of Inter-Turn Short Circuit Faults in PMSMs for Agricultural Machinery Based on Data Fusion and Bayesian Optimization

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