A Performance Evaluation of Two Bispectrum Analysis Methods Applied to Electrical Current Signals for Monitoring Induction Motor-Driven Systems

Baoshan Huang, Guojin Feng, Xiaoli Tang, James Xi Gu, Guanghua Xu, Robert Cattley, Fengshou Gu and Andrew D. Ball
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Baoshan Huang: School of Industrial Automation, Beijing Institute of Technology, Zhuhai 519088, China
Guojin Feng: Department of Mechanical, Aerospace and Civil Engineering, Brunel University London, Uxbridge, Middlesex UB8 3PH, UK
Xiaoli Tang: Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
James Xi Gu: School of Industrial Automation, Beijing Institute of Technology, Zhuhai 519088, China
Guanghua Xu: School of Mechanical Engineering, Xi’an Jiaotong University, Xi’an 710049, China
Robert Cattley: Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
Fengshou Gu: Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK
Andrew D. Ball: Centre for Efficiency and Performance Engineering, University of Huddersfield, Huddersfield HD1 3DH, UK

Energies, 2019, vol. 12, issue 8, 1-23

Abstract: This paper investigates the performance of the conventional bispectrum (CB) method and its new variant, the modulation signal bispectrum (MSB) method, in analysing the electrical current signals of induction machines for the condition monitoring of rotor systems driven by electrical motors. Current signal models which include the phases of the various electrical and magnetic quantities are explained first to show the theoretical relationships of spectral sidebands and their associated phases due to rotor faults. It then discusses the inefficiency of CB and the proficiency of MSB in characterising the sidebands based on simulated signals. Finally, these two methods are applied to analyse current signals measured from different rotor faults, including broken rotor bar (BRB), downstream gearbox wear progressions and various compressor faults, and the diagnostic results show that the MSB outperforms the CB method significantly in that it provides more accurate and sparse diagnostics, thanks to its unique capability of nonlinear modulation detection and random noise suppression.

Keywords: modulation signal bispectrum; higher order spectra; fault diagnosis; induction motor; gearbox; reciprocating compressor (search for similar items in EconPapers)
JEL-codes: Q Q0 Q4 Q40 Q41 Q42 Q43 Q47 Q48 Q49 (search for similar items in EconPapers)
Date: 2019
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (6)

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