Enhanced Particle Filtering for Bearing Remaining Useful Life Prediction of Wind Turbine Drivetrain Gearboxes

• Published in: IEEE transactions on industrial electronics (1982) Vol. 66; no. 6; pp. 4738 - 4748
• Main Authors: Cheng, Fangzhou, Qu, Liyan, Qiao, Wei, Hao, Liwei
• Format: Journal Article
• Language: English
• Published: New York IEEE 01.06.2019; The Institute of Electrical and Electronics Engineers, Inc. (IEEE)
• Subjects: Adaptive algorithms; Adaptive filters; Adaptive systems; Artificial neural networks; Atmospheric measurements; Automation & Control Systems; Bearing; Degradation; Engineering; Enhanced particle filtering (EPF); Fuzzy logic; Fuzzy systems; gearbox; Gearboxes; Induction generators; Instruments & Instrumentation; Life prediction; Mathematical models; Monitoring; Particle measurements; prediction; Prediction algorithms; Predictive models; Prognostics and health management; Reliability aspects; remaining useful life (RUL); Resampling; Turbines; Useful life; Wind power; wind turbine; Wind turbines
• ISSN: 0278-0046; 1557-9948
• DOI: 10.1109/TIE.2018.2866057

Bearing is the major contributor to wind turbine gearbox failures. Accurate remaining useful life prediction for drivetrain gearboxes of wind turbines is of great importance to achieve condition-based maintenance to improve the wind turbine reliability and reduce the cost of wind power. However, remaining useful life prediction is a challenging work due to the limited monitoring data and the lack of an accurate physical fault degradation model. The particle filtering method has been used for the remaining useful life prediction of wind turbine drivetrain gearboxes, but suffers from the particle impoverishment problem due to a low particle diversity, which may lead to unsatisfactory prediction results. To solve this problem, this paper proposes an enhanced particle filtering algorithm in which an adaptive neuro-fuzzy inference system is designed to learn the state transition function in the fault degradation model using the fault indicator extracted from the monitoring data; a particle modification method and an improved multinomial resampling method are proposed to improve the particle diversity in the resampling process to solve the particle impoverishment problem. The enhanced particle filtering algorithm is applied successfully to predict the remaining useful life of a bearing in the drivetrain gearbox of a 2.5 MW wind turbine equipped with a doubly-fed induction generator.