期刊
2018 PROGNOSTICS AND SYSTEM HEALTH MANAGEMENT CONFERENCE (PHM-CHONGQING 2018)
卷 -, 期 -, 页码 1037-1042出版社
IEEE
DOI: 10.1109/PHM-Chongqing.2018.00184
关键词
prognostics and health management; remaining useful life; bidirectional LSTM; deep learning
资金
- National Natural Science Foundation of China [U1534204, U11572206, U11790282, U11672104, U11225212]
- Natural Science Foundation of Hebei Province [A2016210099]
Remaining Useful Life (RUL) estimation plays a crucial role in Prognostics and Health Management (PHM). Traditional RUL estimation models are built on sufficient prior knowledge of critical components degradation process which is not easily available in most situation. With the development of integrated circuit and sensor technique, data-driven approaches show good potential on RUL estimation. This paper proposes a new data-driven approach with Bidirectional Long Short-Term Memory (BiLSTM) network for RUL estimation, which can make full use of the sensor date sequence in bidirection. By visualized analysis of the hidden layers, the model can expose hidden patterns with sensor data of multiple working conditions, fault patterns and degradation model. With experiment using C-MAPSS dataset, BiLSTM approach for RUL estimation outperforms other traditional approaches for RUL estimation.
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