Fault Detection and Diagnosis of PMSM under Unsteady State with Variable Speed and Load Conditions

Zhifu Wang; Chuang Cao; Qiang Song; Wenjiang Li

doi:10.15918/j.jbit1004-0579.201726.S105

Fault Detection and Diagnosis of PMSM under Unsteady State with Variable Speed and Load Conditions

Zhifu Wang^*, Chuang Cao, Qiang Song, Wenjiang Li

^*此作品的通讯作者

机械与车辆学院

科研成果: 期刊稿件 › 文章 › 同行评审

摘要

An integrated fault diagnosis method for stator winding faults is proposed. Based on the abc coordinate mathematical model, the fault model of permanent magnet synchronous motor was defined. Drive control system model was established in Matlab/Simulink after that. The spectrum of three-phase stator current was analyzed by fast-Fourier transform (FFT) signal processing. Harmonic component was extracted as the fault feature vector. To achieve accurate diagnosis under unsteady conditions, the three-layer feed-forward artificial neural network (ANN) and the diagnosis swarm was proposed. The diagnosis method was validated by simulation and experimentation. According to the diagnosis result, a very keen degree of recognition for three type short circuits faults was showed. The accuracy rate is over 87%.

源语言	英语
页（从-至）	28-34
页数	7
期刊	Journal of Beijing Institute of Technology (English Edition)
卷	26
DOI	https://doi.org/10.15918/j.jbit1004-0579.201726.S105
出版状态	已出版 - 1 12月 2017

访问文件

10.15918/j.jbit1004-0579.201726.S105

其它文件与链接

链接到 Scopus 的出版物

引用此

@article{bd3545fc07bc471f871e37dd9afbde01,

title = "Fault Detection and Diagnosis of PMSM under Unsteady State with Variable Speed and Load Conditions",

abstract = "An integrated fault diagnosis method for stator winding faults is proposed. Based on the abc coordinate mathematical model, the fault model of permanent magnet synchronous motor was defined. Drive control system model was established in Matlab/Simulink after that. The spectrum of three-phase stator current was analyzed by fast-Fourier transform (FFT) signal processing. Harmonic component was extracted as the fault feature vector. To achieve accurate diagnosis under unsteady conditions, the three-layer feed-forward artificial neural network (ANN) and the diagnosis swarm was proposed. The diagnosis method was validated by simulation and experimentation. According to the diagnosis result, a very keen degree of recognition for three type short circuits faults was showed. The accuracy rate is over 87%.",

keywords = "Artificial neural network, Fast-Fourier transform(FFT), Fault diagnosis, Permanent magnet synchronous motor (PMSM)",

author = "Zhifu Wang and Chuang Cao and Qiang Song and Wenjiang Li",

note = "Publisher Copyright: {\textcopyright} 2017 Editorial Department of Journal of Beijing Institute of Technology.",

year = "2017",

month = dec,

day = "1",

doi = "10.15918/j.jbit1004-0579.201726.S105",

language = "English",

volume = "26",

pages = "28--34",

journal = "Journal of Beijing Institute of Technology (English Edition)",

issn = "1004-0579",

publisher = "Beijing Institute of Technology",

}

TY - JOUR

T1 - Fault Detection and Diagnosis of PMSM under Unsteady State with Variable Speed and Load Conditions

AU - Wang, Zhifu

AU - Cao, Chuang

AU - Song, Qiang

AU - Li, Wenjiang

PY - 2017/12/1

Y1 - 2017/12/1

N2 - An integrated fault diagnosis method for stator winding faults is proposed. Based on the abc coordinate mathematical model, the fault model of permanent magnet synchronous motor was defined. Drive control system model was established in Matlab/Simulink after that. The spectrum of three-phase stator current was analyzed by fast-Fourier transform (FFT) signal processing. Harmonic component was extracted as the fault feature vector. To achieve accurate diagnosis under unsteady conditions, the three-layer feed-forward artificial neural network (ANN) and the diagnosis swarm was proposed. The diagnosis method was validated by simulation and experimentation. According to the diagnosis result, a very keen degree of recognition for three type short circuits faults was showed. The accuracy rate is over 87%.

AB - An integrated fault diagnosis method for stator winding faults is proposed. Based on the abc coordinate mathematical model, the fault model of permanent magnet synchronous motor was defined. Drive control system model was established in Matlab/Simulink after that. The spectrum of three-phase stator current was analyzed by fast-Fourier transform (FFT) signal processing. Harmonic component was extracted as the fault feature vector. To achieve accurate diagnosis under unsteady conditions, the three-layer feed-forward artificial neural network (ANN) and the diagnosis swarm was proposed. The diagnosis method was validated by simulation and experimentation. According to the diagnosis result, a very keen degree of recognition for three type short circuits faults was showed. The accuracy rate is over 87%.

KW - Artificial neural network

KW - Fast-Fourier transform(FFT)

KW - Fault diagnosis

KW - Permanent magnet synchronous motor (PMSM)

UR - http://www.scopus.com/inward/record.url?scp=85054824419&partnerID=8YFLogxK

U2 - 10.15918/j.jbit1004-0579.201726.S105

DO - 10.15918/j.jbit1004-0579.201726.S105

M3 - Article

AN - SCOPUS:85054824419

SN - 1004-0579

VL - 26

SP - 28

EP - 34

JO - Journal of Beijing Institute of Technology (English Edition)

JF - Journal of Beijing Institute of Technology (English Edition)

ER -

Fault Detection and Diagnosis of PMSM under Unsteady State with Variable Speed and Load Conditions

摘要

访问文件

其它文件与链接

指纹

引用此