Mechanical faults detection for vehicle motors under nonstationary conditions based on Vold-Kalman order tracking method

The fault diagnosis and health management of motors are the key technology that needs to be resolved in the future with the development of electric vehicles. The drive motors usually working under nonstationary conditions. Moreover, the mechanical fault is the most common type among the motor fault types. Therefore, developing the mechanical fault detection method under variable speed conditions would be of great value to improve the diagnosis accuracy of vehicle motors. This paper deals with the mechanism of the bearing fault, the rotor eccentricity fault, and their compound fault of permanent magnet synchronous motors. The purpose of this paper is to propose a mechanical fault detection method based on Vold-Kalman for vehicle motors under variable working conditions. First, the vibration characteristics of the healthy and the faulty motors are analyzed. Next, for eliminating the influence of variable conditions on the mechanical-fault detection, the fault characteristics are extracted by applying the Vold-Kalman filter. Finally, experiments are carried out based on the selected part of the new European driving cycle test conditions. The experimental results show that the proposed approach can detect the mechanical fault of the motor under nonstationary conditions effectively.

• Record URL:
  https://doi.org/10.1177/09544070221078679
• Availability:
  • Find a library where document is available. Order URL: http://worldcat.org/issn/09544070
• Supplemental Notes:
  • © IMechE 2022.
• Authors:
  • Zhao, Sifang
  • 0000-0003-4476-9631
  • Song, Qiang
  • 0000-0002-7733-264X
  • Wang, Mingsheng
  • 0000-0002-6343-1516
  • Lai, Wuxuan
  • Li, Yiting
• Publication Date: 2023-3

Language

• English

Media Info

• Media Type: Web
• Features: References;
• Pagination: pp 839-851
• Serial:
  • Proceedings of the Institution of Mechanical Engineers, Part D: Journal of Automobile Engineering
  • Volume: 237
  • Issue Number: 4
  • Publisher: Sage Publications Limited
  • ISSN: 0954-4070
  • EISSN: 2041-2991
  • Serial URL: http://pid.sagepub.com/content/current

Subject/Index Terms

• TRT Terms: Diagnosis; Electric vehicles; Fault monitoring; Kalman filtering; Motors
• Subject Areas: Highways; Vehicles and Equipment;

Filing Info

• Accession Number: 01878080
• Record Type: Publication
• Files: TRIS
• Created Date: Mar 30 2023 1:18PM