Machine learning approaches for fault detection and diagnosis of induction motors

被引：5

作者：

Belguesmi, Lamia ^{[1
]}

Hajji, Mansour ^{[1
]}

Mansouri, Majdi ^{[2
]}

Harkat, Mohamed-Faouzi ^{[2
]}

Kouadri, Abdelmalek ^{[2
]}

Nounou, Hazem ^{[2
]}

Nounou, Mohamed ^{[2
]}

机构：

[1] Univ Kairouan, Higher Inst Appl Sci & Technol Kasserine, Kasserine, Tunisia

[2] Texas A&M Univ Qatar, Elect & Comp Engn Program, Doha, Qatar

来源：

PROCEEDINGS OF THE 2020 17TH INTERNATIONAL MULTI-CONFERENCE ON SYSTEMS, SIGNALS & DEVICES (SSD 2020) | 2020年

关键词：

Induction motor; machine learning (ML); principal component analysis (PCA); feature extraction; fault diagnosis; fault classification; EXTRACTION;

D O I：

10.1109/SSD49366.2020.9364240

中图分类号：

TM [电工技术]; TN [电子技术、通信技术];

学科分类号：

0808 ; 0809 ;

摘要：

This paper deals with the problem of monitoring of induction motors (IM) through the development of fault detection and diagnosis (FDD) approach. The developed FDD technique is addressed such that, the principal component analysis (PCA) technique is used for features extraction purposes and the machine learning (ML) classifiers are applied for fault diagnosis. In the proposed FDD approach the most efficient features are extracted and selected through PCA scheme using induction motor data. Besides, their statistical characteristics (mean and variance) are also included. The ML classifiers are applied using the extracted and selected features to perform the FDD problem. The obtained results indicate that the proposed techniques have a wide application area, fast fault detection and diagnosis, making them more reliable for induction motors monitoring.

引用

页码：692 / 698

页数：7

共 23 条

[1]

Breiman L., 1984, Classification and Regression Trees

[2] Inter-turn stator winding fault diagnosis in three-phase induction motors, by Park's vector approach [J].

Cardoso, AJM ;

Cruz, SMA ;

Fonseca, DSB .

IEEE TRANSACTIONS ON ENERGY CONVERSION, 1999, 14 (03) :595-598

[3] SUPPORT-VECTOR NETWORKS [J].

CORTES, C ;

VAPNIK, V .

MACHINE LEARNING, 1995, 20 (03) :273-297

[4] Recent Advances in Modeling and Online Detection of Stator Interturn Faults in Electrical Motors [J].

Gandhi, Arun ;

Corrigan, Timothy ;

Parsa, Leila .

IEEE TRANSACTIONS ON INDUSTRIAL ELECTRONICS, 2011, 58 (05) :1564-1575

[5] RETRACTED: Fault diagnosis of heating systems using multivariate feature extraction based machine learning classifiers (Retracted article. See vol. 41, 2021) [J].

Gharsellaoui, Sondes ;

Mansouri, Majdi ;

Trabelsi, Mohamed ;

Refaat, Shady S. ;

Messaoud, Hassani .

JOURNAL OF BUILDING ENGINEERING, 2020, 30

[6] Multivariate Features Extraction and Effective Decision Making Using Machine Learning Approaches [J].

Gharsellaoui, Sondes ;

Mansouri, Majdi ;

Refaat, Shady S. ;

Abu-Rub, Haitham ;

Messaoud, Hassani .

ENERGIES, 2020, 13 (03)

[7] Experimental validation of doubly fed induction machine electrical faults diagnosis under time-varying conditions [J].

Gritli, Yasser ;

Stefani, Andrea ;

Rossi, Claudio ;

Filippetti, Fiorenzo ;

Chatti, Abderrazak .

ELECTRIC POWER SYSTEMS RESEARCH, 2011, 81 (03) :751-766

[8] Fault diagnosis of rotating machinery based on improved wavelet package transform and SVMs ensemble [J].

Hu, Qiao ;

He, Zhengjia ;

Zhang, Zhousuo ;

Zi, Yanyang .

MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2007, 21 (02) :688-705

[9] Class-specific attribute weighted naive Bayes [J].

Jiang, Liangxiao ;

Zhang, Lungan ;

Yu, Liangjun ;

Wang, Dianhong .

PATTERN RECOGNITION, 2019, 88 :321-330

[10] Three-phase current injection method for mitigating turn-to-turn short-circuit fault in concentrated-winding permanent magnet aircraft starter generator [J].

Jiang, Yunyi ;

Zhang, Zhuoran ;

Jiang, Wenying ;

Geng, Weiwei ;

Huang, Jian .

IET ELECTRIC POWER APPLICATIONS, 2018, 12 (04) :566-574

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