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RUL prediction method for high-speed bearings based on SSF and ENN

Published:2022-11-22 author:ZHAO Yan-li, WANG Wen-yuan, HE Jin Browse: 488 Check PDF documents

RUL prediction method for high-speed bearings based on SSF and ENN

ZHAO Yan-li1, WANG Wen-yuan2, HE Jin2

(1.Department of Information Technology, Zhengzhou Vocational College of Finance, Taxation and Finance,

Zhengzhou 450003, China; 2.School of Mechanical and Electrical Engineering, Henan University of

Technology, Zhengzhou 450001, China)

Abstract: Effective failure prediction and health management (PHM) for wind turbine gearbox bearings can reduce the failure rate of wind turbine bearing components. Aiming at the problem of accurate prediction of remaining residual service life (RUL) of high-speed bearing of wind turbine, a data-driven RUL prediction method for wind turbine high-speed bearings based on spectral shape factor (SSF) and Elman neural network (ENN) data was proposed. Firstly, Teager energy operator (TEO) was introduced to preprocess the original vibration signal of high-speed shaft of wind turbine. Secondly, based on short-time Fourier transform (STFT), an SSF was constructed to transform each fault characteristic index of the bearing. Then, based on the monotonicity, trendability and predictability of TEO energy signal, the fitness function was constructed, the transformed indexes were screened, and the fault characteristic index most suitable for predicting the bearing RUL was determined. The RUL prediction experiment of a specific wind turbine highspeed bearing in actual operation was carried out by using ENN and measured data. Finally, the method based on SSF and ENN was quantitatively compared with three existing data-driven techniques. The results show that after TEO pretreatment and SSF transformation, the fitness of logarithmic entropy of the vibration signal is the highest. At the same time, compared with the other three kinds of data-driven techniques, the RUL prediction method has higher RUL prediction accuracy, which can maintain a high level within 35 days. Thus, the applicability and rationality of the RUL prediction method can be well proven.

Key words: high-speed bearings; spectral shape factor (SSF); Elman neural network(ENN); remaining useful life(RUL); shorttime Fourier transform(STFT); datadriven; logarithmic entropy; Teager energy operator (TEO)

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