期刊
MEASUREMENT
卷 166, 期 -, 页码 -出版社
ELSEVIER SCI LTD
DOI: 10.1016/j.measurement.2020.108215
关键词
Journal bearing; Seizure failure; Degradation stage division; RUL prediction; LSTM neural network; PSO
资金
- Marine Low Speed Engine Project-Phase I [CDGC01-KT11]
Accurate bearing degradation assessment and remaining useful life (RUL) prediction may effectively avoid major disasters in manufacturing. With the rapid development of the computer industry, deep learning has emerged as a reliable algorithm for time-series prediction and has shown good performance. In this paper, the journal bearing seizure experiment was performed. The collected multi-sensor failure dataset is used for feature extraction and degradation indicator (DI) construction. The DI and working condition information are applied for degradation stage (DS) division by the fuzzy c-means (FCM) algorithm. Considering the transition of different DSs, the one-stage and multi-stage iteration prediction models based on the Long Short-Term Memory (LSTM) neural network for RUL prediction are proposed. The particle swarm optimization (PSO) is used to optimize model hyperparameters. The results show that the multi-stage iteration prediction may achieve the early warning of seizure failure and outperform the one-stage iteration prediction and traditional machine learning prediction. (C) 2020 Published by Elsevier Ltd.
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