Remaining useful life prediction for equipment based on LSTM encoder-decoder method

ZHAO Zhi-hong; LI Qing; LI Le-hao; ZHAO Jing-jiao

doi:10.19818/j.cnki.1671-1637.2021.06.021

Volume 21 Issue 6

Dec. 2021

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Article Navigation > Journal of Traffic and Transportation Engineering > 2021 > 21(6): 269-277

ZHAO Zhi-hong, LI Qing, LI Le-hao, ZHAO Jing-jiao. Remaining useful life prediction for equipment based on LSTM encoder-decoder method[J]. Journal of Traffic and Transportation Engineering, 2021, 21(6): 269-277. doi: 10.19818/j.cnki.1671-1637.2021.06.021

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ZHAO Zhi-hong, LI Qing, LI Le-hao, ZHAO Jing-jiao. Remaining useful life prediction for equipment based on LSTM encoder-decoder method[J]. Journal of Traffic and Transportation Engineering, 2021, 21(6): 269-277. doi: 10.19818/j.cnki.1671-1637.2021.06.021

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Remaining useful life prediction for equipment based on LSTM encoder-decoder method

doi: 10.19818/j.cnki.1671-1637.2021.06.021

1.
School of Information Science and Technology, Shijiazhuang Tiedao University, Shijiazhuang 050043, Hebei, China
2.
State Key Laboratory of Mechanical Behavior and System Safety of Traffic Engineering Structures, Shijiazhuang Tiedao University, Shijiazhuang 050043, Hebei, China

Funds:

National Natural Science Foundation of China 11972236

National Natural Science Foundation of China 11790282

Graduate Innovation Support Project of Shijiazhuang Tiedao University YC2021077

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Author Bio:
ZHAO Zhi-hong(1972-), male, professor, PhD, hb_zhaozhihong@126.com
Received Date: 2021-05-20
Available Online: 2022-02-11
Publish Date: 2021-12-01

Abstract

Abstract

A remaining useful life (RUL) prediction model of mechanical equipment was established based on the long short-term memory (LSTM) encoder-decoder method. The acquired sensor data were preprocessed. The data sequence was coded using the LSTM encoder method. An intermediate representation of the equipment status information was obtained. The characteristic information of the equipment status was obtained in the intermediate representation of the equipment status information. The intermediate representation information was decoded using the LSTM decoder method, and the RUL was predicted using the decoded information. RUL prediction experiments of the LSTM encoder-decoder method on open C-MAPSS data sets were performed. The LSTM encoder-decoder method was compared with the LSTM method, deep-LSTM (D-LSTM) method, and other methods. The effect of the sliding window size on RUL prediction results was evaluated. Research results show that scoring function values and root mean square error (RMSE) evaluation indexes of the RUL prediction results of the LSTM encoder-decoder method are more accurate than those of the LSTM method and D-LSTM method. In the FD001 subset, the RMSEs of the LSTM encoder-decoder method, LSTM method, and D-LSTM method are 11, 12, and 16, respectively. When the sliding window size is 30, the scoring function values corresponding to the FD001-FD004 subsets of the LSTM encoder-decoder method are 164, 3 012, 372, and 4 800, and the corresponding RMSEs are 11, 20, 14, and 22. When the sliding window size increases to 40, the respective scoring function values are 305, 1 220, 408, and 4 828, and the corresponding RMSEs are 14, 16, 15, and 19. Therefore, the proposed method based on the LSTM encoder-decoder effectively predicts the RUL of mechanical equipment, and the sliding window size significantly influences the RUL prediction results. 4 tabs, 6 figs, 32 refs.
- remaining useful life prediction,
- encoder-decoder,
- LSTM,
- deep learning,
- feature extraction

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References(32)

References

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Remaining useful life prediction for equipment based on LSTM encoder-decoder method

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