Bearing remaining useful life prediction with convolutional long short-term memory fusion networks

Shaoke Wan, Xiaohu Li, Yanfei Zhang, Shijie Liu, Jun Hong and Dongfeng Wang

Reliability Engineering and System Safety, 2022, vol. 224, issue C

Abstract: Deep learning methods have improved the performance of RUL prediction, and multi-sensor data has also been found can significantly improve the fault diagnosis's accuracy. Hence, it is also highly motivated to integrate the deeply learned features from multi-sensor data for RUL prediction. In this paper, a novel deep learning framework with multi-branch networks, which is called convolutional long short-term memory fusion networks (CLSTMF), is proposed for RUL prediction with multi-sensor data. In each branch networks, shallow features of single sensor's data are extracted by convolutional layer of convolutional neural network (CNN), and then convolutional long short-term memory (CLSTM) network is employed to capture deep temporal features from these shallow features. Meanwhile, a novel information transfer layer (ITL) is developed to fuse the multi-sensor data's features captured with CLSTM in different branch networks. Experiments are also performed on two real run-to-failure datasets and the results indicates that the proposed approach performs well with respect to higher accuracy.

Keywords: Prognostics and health management; Remaining useful life prediction; Feature fusion; Convolutional long short-term memory (CLSTM) network (search for similar items in EconPapers)
Date: 2022
References: View references in EconPapers View complete reference list from CitEc
Citations: View citations in EconPapers (12)

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Persistent link: https://EconPapers.repec.org/RePEc:eee:reensy:v:224:y:2022:i:c:s095183202200182x

DOI: 10.1016/j.ress.2022.108528

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