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Diagnosis of Mechanical Unbalance Fault in Permanent Magnet Synchronous Machine Drives
Abstract
In recent years, motor current signature analysis has been used for fault diagnosis of permanent magnet synchronous machines. However, the fault feature is not obvious and is easily hidden in the main component, since the amplitudes of fault harmonic components are quiet small compared with the fundamental component. A new method to diagnose the mechanical unbalance fault effectively in permanent magnet synchronous machine drives is proposed in this article, where Park's vector is used to eliminate the fundamental component from the fast Fourier transform (FFT) results of motor current signature analysis; the fault feature is then analyzed by discrete wavelet transform. The effectiveness of the proposed method is validated by simulation and experimental results. It is shown that the proposed diagnosis method may work well under different control strategies.
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The above mentioned signals are available for the detection system even if other control structures (DTC) are applied, as they can be easily calculated.
“The above mentioned signals are available for the detection system, even if other control structures (DTC) are applied [134], [165], as they can be easily calculated, e.g. with measured stator currents and rotor angle.”
From Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges (2022) · cites this paper as [165]
In [165] and [166], DWT analysis of Park-transformed stator current confirmed effectiveness for unbalance detection under non-stationary conditions by simulation and experiments.
“The effectiveness of this method was confirmed by simulation and experimental results carried out under non-stationary conditions.”
From Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges (2022) · cites this paper as [165]
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Fault Diagnosis and Fault-Tolerant Control of PMSM Drives–State of the Art and Future Challenges
Orlowska-Kowalska, T., Wolkiewicz, M., Pietrzak, P., Skowron, M., Ewert, P., Tarchala, G., Krzysztofiak, M., & Kowalski, C. T. (2022) · IEEE Access · doi:10.1109/access.2022.3180153 · Cited by 328
Metadata
- Authors
- Zhang, J., Hang, J., & Cheng, M.
- Affiliations
- Southeast University (CN)
- Corresponding author
- Zhang, Jianzhong
- Venue
- Electric Power Components and Systems
- Publisher
- Informa UK Limited
- Published
- 2016-06-17
- Volume
- 44
- Issue
- 12
- Pages
- 1408-1417
- ISSN
- 1532-5008 (ISSN-L)
- 1532-5016
- OpenAlex
- W2443328068
- Cited by
- 23
- References
- 24
- Citations by year
- 2026: 3 · 2025: 1 · 2024: 5 · 2023: 2 · 2022: 3 · 2020: 3 · 2019: 3 · 2018: 1 · 2017: 2
- FWCI
- 1.68
- Type
- journal article
- Language
- en
- Open access
- closed
- Locations
- Electric Power Components and Systems (publishedVersion)
- Topics
- Physical Sciences › Engineering › Control and Systems Engineering › Machine Fault Diagnosis Techniques
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Electric Motor Design and Analysis
- Physical Sciences › Engineering › Electrical and Electronic Engineering › Sensorless Control of Electric Motors
- Keywords
- permanent magnet synchronous motor, motor current signature analysis, discrete wavelet transform, fault feature extraction, fault diagnosis, machine drives
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