1. Health state assessment of bearing with feature enhancement and prediction error compensation strategy.
- Author
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Zhang, Yong, Sun, Jiahua, Zhang, Jing, Shen, Haoran, She, Yingying, and Chang, Yang
- Subjects
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HILBERT-Huang transform , *RECURRENT neural networks , *INDUSTRIAL safety , *KALMAN filtering , *FORECASTING - Abstract
Bearing is one of the most important component of rotary machine, and its health state is directly related to the safety of industrial production. In this paper, health state assessment of bearing is investigated with feature enhancement and prediction error compensation. Specially, health state assessment consists of time-to-start prediction point detection and remaining useful life (RUL) prediction. In the first stage, variance feature based on Kalman filter is introduced to detect the time-to-start prediction point. Subsequently, complete ensemble empirical mode decomposition with adaptive noise is employed to reconstruct the degradation trend, and cumulative function is adopted to realize the feature enhancement, then efficient health indicator can be constructed. In the RUL prediction stage, degradation model and adaptive extended Kalman filter are fused to achieve the prediction, and bidirectional gated recurrent unit neural network is chosen to compensate the prediction error. Finally, experimental studies based on PRONOSTIA and XJTU-SY datasets are conducted to validate the effectiveness of the proposed method and its superiority over the traditional methods. • The proposed method provides a new solution to determine the prediction start time. • The cumulative function is used to establish the trend health indicator. • The adaptive EKF and Bi-GRU are fused to predict the RUL of bearing. • Different bearing datasets are used to verify the effectiveness of proposed method. [ABSTRACT FROM AUTHOR]
- Published
- 2023
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