Algorithm Research & Explore
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App. Res. of Comp. Printed Article
Vol. 38, 2021 No. 7
2103-2107

Prediction of bearing remaining useful life based on parallel CNN-SE-Bi-LSTM

Cao Zhengzhi
Ye Chunming
Business School, University of Shanghai for Science & Technology, Shanghai 200093, China
DOI: 10.19734/j.issn.1001-3695.2020.08.0224

Abstract

As a kind of mechanical standard parts, rolling bearing is widely used in all kinds of rotating machinery. Its health condition is very important for the normal operation of equipments. Mastering its remaining useful life(RUL) can better ensure the security and efficiency of production activities. Aiming at the common problems of current machine RUL prediction methods based on deep learning: a) the prediction performance largely depends on manual feature design, b) the models cannot fully extract the useful features from the data, c) the multi-sensor data is not explicitly considered in the learning process. This paper proposed a new equipment RUL prediction network: parallel a set of integrated network comprised of CNN network with SE mechanism(CNN-SE-Net) and Bi-LSTM network(CNN-SE-Bi-LSTM). It directly used data from different sensors as inputs to the prediction network, and extracted spatial features with the improved CNN-SE-Net, extracted the temporal features with Bi-LSTM to establish several independent branches of RUL prediction model which could automatically learn high-level representation from input data. It obtained the final RUL prediction model by paralleling the features learned from the branches with the fully connection layer. The effectiveness of the proposed network was verified by the data of accelerated degradation test of rolling bearing, and comparing experiments with some existing improved algorithms. The results show that, face with the original multi-sensor data, the algorithm can adaptively provide accurate RUL prediction results, and the prediction performance is better than some existing prediction methods.

Foundation Support

国家自然科学基金资助项目(71840003)
上海理工大学科技发展基金资助项目(2018KJFZ043)

Publish Information

DOI: 10.19734/j.issn.1001-3695.2020.08.0224
Publish at: Application Research of Computers Printed Article, Vol. 38, 2021 No. 7
Section: Algorithm Research & Explore
Pages: 2103-2107
Serial Number: 1001-3695(2021)07-034-2103-05

Publish History

[2021-07-05] Printed Article

Cite This Article

曹正志, 叶春明. 基于并联CNN-SE-Bi-LSTM的轴承剩余使用寿命预测 [J]. 计算机应用研究, 2021, 38 (7): 2103-2107. (Cao Zhengzhi, Ye Chunming. Prediction of bearing remaining useful life based on parallel CNN-SE-Bi-LSTM [J]. Application Research of Computers, 2021, 38 (7): 2103-2107. )

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  • Application Research of Computers Monthly Journal
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Application Research of Computers, founded in 1984, is an academic journal of computing technology sponsored by Sichuan Institute of Computer Sciences under the Science and Technology Department of Sichuan Province.

Aiming at the urgently needed cutting-edge technology in this discipline, Application Research of Computers reflects the mainstream technology, hot technology and the latest development trend of computer application research at home and abroad in a timely manner. The main contents of the journal include high-level academic papers in this discipline, the latest scientific research results and major application results. The contents of the columns involve new theories of computer discipline, basic computer theory, algorithm theory research, algorithm design and analysis, blockchain technology, system software and software engineering technology, pattern recognition and artificial intelligence, architecture, advanced computing, parallel processing, database technology, computer network and communication technology, information security technology, computer image graphics and its latest hot application technology.

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