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Smart Structures and Systems
  Volume 29, Number 6, June 2022 , pages 799-805
DOI: https://doi.org/10.12989/sss.2022.29.6.799
 


Remaining useful life prediction for PMSM under radial load using particle filter
Younghun Lee, Inhwan Kim, Sikgyoung Choi, Jaewook Oh and Namsu Kim

 
Abstract
    Permanent magnet synchronous motors (PMSMs) are widely used in systems requiring high control precision, efficiency, and reliability. Predicting the remaining useful life (RUL) with health monitoring of PMSMs prevents catastrophic failure and ensures reliable operation of system. In this study, a model-based method for predicting the RUL of PMSMs using phase current and vibration signals is proposed. The proposed method includes feature selection and RUL prediction based on a particle filter with a degradation model. The Paris-Erdogan model describing micro fatigue crack propagation is used as the degradation model. An experimental set-up to conduct accelerated life test, capable of monitoring various signals was designed in this study. Phase current and vibration data obtained from an accelerated life test of the PMSMs were used to verify the proposed approach. Features extracted from the data were clustered based on monotonicity and correlation clustering, respectively. The results identify the effectiveness of using the current data in predicting the RUL of PMSMs.
 
Key Words
    accelerated life testing; particle filter; PMSM; RUL prediction
 
Address
Younghun Lee, Inhwan Kim, Sikgyoung Choi, Jaewook Oh and Namsu Kim: Department of Mechanical Engineering, Konkuk University, 120 Neungdong-ro, Gwangjin-gu, Seoul, Republic of Korea
 

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