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Kalman Filter-Based Systems Approach for Prognostics and Health Management of Electric Motors

In: Advances in Reliability and Maintainability Methods and Engineering Applications

Author

Listed:
  • Hyung Jun Park

    (Korea Aerospace University)

  • Dongwoo Lee

    (D&SHINE Research Institute)

  • Seokgoo Kim

    (University of Florida)

  • Nam Ho Kim

    (University of Florida)

  • Joo-Ho Choi

    (Korea Aerospace University)

Abstract

A Kalman filter-based framework is proposed for the prognostics and health management of DC electric motors by treating them as a system. The control signals of the motor are used to estimate the current health and predict the remaining useful life (RUL) of the motor and its components, such as bearings and permanent magnets. The framework consists of an online health diagnosis to estimate the health status of the motor and each component, and an offline failure prognosis to predict the RULs. The approach is demonstrated with the aid of two real examples: the reaction wheel motor for advanced attitude control of satellites and the driving motors in a quadcopter to lift and control flight operations. In each example, the motors were subjected to accelerated degradation tests, motor control data were collected for each cycle, and RULs were predicted against failure thresholds critical to motor performance. The results showed that the framework can be used to effectively predict the RUL of a degraded motor, thereby enabling failure prevention and proactive maintenance scheduling.

Suggested Citation

  • Hyung Jun Park & Dongwoo Lee & Seokgoo Kim & Nam Ho Kim & Joo-Ho Choi, 2023. "Kalman Filter-Based Systems Approach for Prognostics and Health Management of Electric Motors," Springer Series in Reliability Engineering, in: Yu Liu & Dong Wang & Jinhua Mi & He Li (ed.), Advances in Reliability and Maintainability Methods and Engineering Applications, pages 515-544, Springer.
    • Handle: RePEc:spr:ssrchp:978-3-031-28859-3_21
    DOI: 10.1007/978-3-031-28859-3_21
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