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Particle Filtering Based Remaining Useful Life Prediction for Electromagnetic Coil Insulation
Haifeng Guo1,2,3,4,5, Aidong Xu1,2,3, Kai Wang1,2,3
1Key Laboratory of Networked Control Systems, Chinese Academy of Sciences, Shenyang 110016, China.
Sensors (Basel, Switzerland)
|January 14, 2021
Summary
This study monitors electromagnetic coil insulation degradation and predicts remaining useful life (RUL). A novel health indicator and particle filtering model enable accurate RUL prediction for predictive maintenance.
Area of Science:
- Electrical Engineering
- Materials Science
- Reliability Engineering
Background:
- Electromagnetic coils are critical system components.
- Insulation failure in coils can lead to severe system malfunctions.
- Effective monitoring and prediction of insulation health are essential for system reliability.
Purpose of the Study:
- To develop a method for monitoring insulation degradation in electromagnetic coils.
- To predict the remaining useful life (RUL) of coil insulation.
- To enable predictive maintenance strategies for systems utilizing electromagnetic coils.
Main Methods:
- Extraction of insulation degradation characteristics from high-frequency electrical parameters.
- Definition of a health indicator based on degradation characteristics.
- Construction of an insulation degradation model and RUL prediction using particle filtering.
Main Results:
- Successful extraction of key insulation degradation features.
- Development of a reliable health indicator for coil insulation.
- Validation of the RUL prediction accuracy through thermal accelerated degradation experiments.
Conclusions:
- The proposed method accurately predicts the remaining useful life of electromagnetic coil insulation.
- The developed health indicator effectively quantifies insulation degradation.
- This approach offers significant potential for implementing predictive maintenance in systems with electromagnetic coils.
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