Remaining useful life prediction of multivariate degradation equipment based on CDBN and BiLSTM

MU Hanxiao; ZHENG Jianfei; HU Changhua; ZHAO Ruixing; DONG Qing

doi:10.7527/S1000-6893.2021.25403

ACTA AERONAUTICAET ASTRONAUTICA SINICA >

2022 , Vol. 43 >Issue 7: 325403 - 325403

DOI: https://doi.org/10.7527/S1000-6893.2021.25403

Electronics and Electrical Engineering and Control

Remaining useful life prediction of multivariate degradation equipment based on CDBN and BiLSTM

MU Hanxiao ,
ZHENG Jianfei ,
HU Changhua ,
ZHAO Ruixing ,
DONG Qing

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1. College of Missile Engineering, Rocket Force University of Engineering, Xi'an 710025, China;
2. The Third Military Representative Office of the Rocket Force Equipment Department in Xi'an, Xi'an 710100, China

Received date: 2021-02-18

Revised date: 2021-05-11

Online published: 2021-05-10

Supported by

National Natural Science Foundation of China (61922089, 61773386, 61833016); Natural Science Foundation of Shaanxi Province (2020JM-360)

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Abstract

Monitoring the multiple health status of complex industrial equipment based on multiple sensors has been gradually applied to achieve more comprehensive and accurate performance evaluation, remaining useful life prediction and health management of the equipment. For a type of multivariate degradation equipment with the monitoring data of large-scale, non-linear, high-dimensional characteristics, a new method based on the Continuous Deep Belief Network (CDBN) and Bidirectional Long Short-Term Memory (BiLSTM) network to predict the remaining useful life of the equipment is proposed. First, the degradation data obtained through the CDBN are analyzed, and the health indexes that reflect the hidden deep-level features of the multivariate degradation equipment are extracted. Then, according to the constructed health indexes, the BiLSTM network is used to mine the timing information and degradation trends, so as to predict the remaining useful life. Finally, the Monte Carlo simulation technology is used to obtain the interval estimation of the remaining useful life. The validity the proposed method is verified through the commercial modular aviation propulsion system dataset. The results show that the method proposed can effectively improve the accuracy of the remaining useful life prediction of this type of equipment, and has potential applicability.

Key words： multivariate degradation equipment; remaining useful life prediction; health index; Continuous Deep Belief Network (CDBN); Bidirectional Long Short-Term Memory(BiLSTM) networkhttp

Cite this article

MU Hanxiao , ZHENG Jianfei , HU Changhua , ZHAO Ruixing , DONG Qing . Remaining useful life prediction of multivariate degradation equipment based on CDBN and BiLSTM[J]. ACTA AERONAUTICAET ASTRONAUTICA SINICA, 2022 , 43(7) : 325403 -325403 . DOI: 10.7527/S1000-6893.2021.25403

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