LAPSE:2023.21367
Published Article
LAPSE:2023.21367
Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection
Jing Tang, Yongheng Yang, Jie Chen, Ruichang Qiu, Zhigang Liu
March 22, 2023
Inverter-fed induction motors (IMs) contain a serious of current harmonics, which become severer under stator and rotor faults. The resultant fault components in the currents affect the monitoring of the motor status. With this background, the fault components in the electromagnetic torque under stator faults considering harmonics are derived in this paper, and the fault components in current harmonics under rotor faults are analyzed. More importantly, the monitoring based on the fault characteristics (both in the torque and current) is proposed to provide reliable stator and rotor fault diagnosis. Specifically, the fault components induced by stator faults in the electromagnetic torque are discussed in this paper, and then, fault components are characterized in the torque spectrum to identify stator faults. To achieve so, a full-order flux observer is adopted to calculate the torque. On the other hand, under rotor faults, the sidebands caused by time and space harmonics in the current are analyzed and exploited to recognize rotor faults, being the motor current signature analysis (MCSA). Experimental tests are performed on an inverter-fed 2.2 kW/380 V/50 Hz IM, which verifies the analysis and the effectiveness of the proposed fault diagnosis methods of inverter-fed IMs.
Keywords
characteristics analysis, Fault Detection, induction motor, rotor fault, stator fault, torque estimation
Suggested Citation
Tang J, Yang Y, Chen J, Qiu R, Liu Z. Characteristics Analysis and Measurement of Inverter-Fed Induction Motors for Stator and Rotor Fault Detection. (2023). LAPSE:2023.21367
Author Affiliations
Tang J: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China [ORCID]
Yang Y: Department of Energy Technology, Aalborg University, 9220 Aalborg, Denmark [ORCID]
Chen J: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China [ORCID]
Qiu R: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China
Liu Z: School of Electrical Engineering, Beijing Jiaotong University, Beijing 100044, China; Beijing Engineering Research Center for Electrical Rail Transit, Beijing 100044, China
Journal Name
Energies
Volume
13
Issue
1
Article Number
E101
Year
2019
Publication Date
2019-12-24
Published Version
ISSN
1996-1073
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PII: en13010101, Publication Type: Journal Article
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doi:10.3390/en13010101
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