Health state assessment of bearing with feature enhancement and prediction error compensation strategy

作者：Zhang, Yong; Sun, Jiahua; Zhang, Jing; Shen, Haoran; She, Yingying; Chang, Yang^*

来源：MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 182: 109573.

DOI：10.1016/j.ymssp.2022.109573

• Remaining useful life prediction
• Time-to-start prediction point detection
• Adaptive extended Kalman filter
• Feature enhancement
• Prediction error compensation

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.

• 单位
  电子科技大学; y