Early Intermittent Interturn Fault Detection and Localization for a Permanent Magnet Synchronous Motor of Electrical Vehicles Using Wavelet Transform

Obeid, Najla Haje; Battiston, Alexandre; Boileau, Thierry; Nahid‐Mobarakeh, Babak

doi:10.1109/tte.2017.2743419

Cited by 75 publications

(17 citation statements)

References 24 publications

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“…The WT, in turn, allows for noise reduction and the selection of frequency bands on which the analysis is to be focused [43]. Stator winding fault results in an increase in the value of details, which cover the frequency range, in which there are components characteristic for this type of damage [44]. The use of WT for the diagnosis of stator winding damage to PMSM drives is associated with complications resulting from the presence in the spectrum of frequency components independent of the damage, which also penetrate the bands where only damage symptoms should be present.…”

Section: Selected Methods For the Pmsm Stator Current Signal Preprocessingmentioning

confidence: 99%

Comparison of Selected Methods for the Stator Winding Condition Monitoring of a PMSM Using the Stator Phase Currents

Pietrzak

Wolkiewicz

2021

Energies

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Stator winding faults are one of the most common faults of permanent magnet synchronous motors (PMSMs), and searching for methods to efficiently detect this type of fault and at an early stage of damage is still an ongoing, important topic. This paper deals with the selected methods for detecting stator winding faults (short-circuits) of a permanent magnet synchronous motor, which are based on the analysis of the stator phase current signal. These methods were experimentally verified and their effectiveness was carefully compared. The article presents the results of experimental studies obtained from the spectral analysis of the stator phase current, stator phase current envelope, and the discrete wavelet transform. The original fault indicators (FIs) based on the observation of the symptoms of stator winding fault were distinguished using the aforementioned methods, which clearly show which symptom is most sensitive to the incipient fault of the stator winding of PMSMs.

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Section: Selected Methods For the Pmsm Stator Current Signal Preprocessingmentioning

confidence: 99%

Comparison of Selected Methods for the Stator Winding Condition Monitoring of a PMSM Using the Stator Phase Currents

Pietrzak

Wolkiewicz

2021

Energies

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“…And they also focused on mechanical faults [70]. Obeid et al used the method based on WT to identify the short circuit winding phase in PMSM [71].…”

Section: Wavelet Transformmentioning

confidence: 99%

Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review

et al. 2019

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Permanent magnet synchronous motors (PMSM) have been used in a lot of industrial fields. In this paper, a review of faults and diagnosis methods of PMSM is presented. Firstly, the electrical, mechanical and magnetic faults of the permanent magnet synchronous motor are introduced. Next, common fault diagnosis methods, such as model-based fault diagnosis, different signal processing methods, and data-driven diagnostic algorithms are enumerated. The research summarized in this paper mainly includes fault performance, harmonic characteristics, different time-frequency analysis techniques, intelligent diagnosis algorithms proposed recently and so on.

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“…Methods that perform time-frequency analysis do not suffer from these limitations. These methods include the application of the continuous wavelet transform (Haje Obeid et al, 2017;Park et al, 2019), Hilbert-Huang transform (Urresty et al, 2009), Wigner-Ville transform also known in the literature as Wigner-Ville distribution (Rosero et al, 2009), Gabor expansion (Dogan and Tetik, 2021) and short-time Fourier transform (STFT) (Maqsood et al, 2020). The STFT analysis has been verified in the past too in the field of PMSM faults diagnosis (Rosero et al, 2007;Zanardelli et al, 2007).…”

Section: Introductionmentioning

confidence: 99%

Stator Winding Fault Detection of Permanent Magnet Synchronous Motors Based on the Short-Time Fourier Transform

Pietrzak

Wolkiewicz

2022

Power Electronics and Drives

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In modern drive systems, the high-efficient permanent magnet synchronous motors (PMSMs) have become one of the most substantial components. Nevertheless, such machines are exposed to various types of faults. Hence, on-line condition monitoring and fault diagnosis of PMSMs have become necessary. One of the most common PMSM faults is the stator winding fault. Due to the destructive character of this failure, it is necessary to use fault diagnostic methods that allow fault detection at its early stage. The article presents the results of experimental studies obtained from fast Fourier transform (FFT) and short-time Fourier transform (STFT) analyses of the stator phase current, stator phase current envelope and stator phase current space vector module. The superiority of the proposed method over the classical approach based on the stator current analysis using FFT is highlighted. The proposed solution is experimentally verified under various motor operating conditions. The application of STFT analysis discussed so far in the literature has been limited to the fault diagnosis of induction motors and the narrow range of the analysed motor operating conditions. Moreover, there are no works in the field of motor diagnostics dealing with STFT analysis for stator windings based on the stator current envelope and the stator current space vector module.

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Early Intermittent Interturn Fault Detection and Localization for a Permanent Magnet Synchronous Motor of Electrical Vehicles Using Wavelet Transform

Cited by 75 publications

References 24 publications

Comparison of Selected Methods for the Stator Winding Condition Monitoring of a PMSM Using the Stator Phase Currents

Comparison of Selected Methods for the Stator Winding Condition Monitoring of a PMSM Using the Stator Phase Currents

Faults and Diagnosis Methods of Permanent Magnet Synchronous Motors: A Review

Stator Winding Fault Detection of Permanent Magnet Synchronous Motors Based on the Short-Time Fourier Transform

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