Remaining useful life prediction of bearing based on autoencoder-LSTM

Chengwang Guo; Yaohua Deng; Chunfeng Zhang; Chang Deng

doi:10.1117/12.2611404

14 October 2021 Remaining useful life prediction of bearing based on autoencoder-LSTM

Chengwang Guo, Yaohua Deng, Chunfeng Zhang, Chang Deng

Proceedings Volume 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation; 119300J (2021) https://doi.org/10.1117/12.2611404
Event: International Conference on Mechanical Engineering, Measurement Control, and Instrumentation (MEMCI 2021), 2021, Guangzhou, China

Abstract

Bearings are the key components of injection molding machines. It is of great significance to accurately assess the degradation of bearings in operation and predict their remaining useful life. With the rapid development of modern industry and the increasing complexity of equipment, model-based methods are difficult to adapt to changing environments, and data-driven methods have been extensively developed. This paper proposes an Autoencoder-LSTM remaining useful life prediction method, which uses Autoencoder to extract features from the original bearing data, and uses LSTM to realize the life prediction of the bearing. Experiments on the PU dataset verify the effectiveness of the features extracted by this method, and compare with other methods to prove the superiority of the method; then the life prediction experiment on the XJTU dataset verifies that the method has higher prediction accuracy, which is better than traditional machine learning and related methods.

Citation Download Citation

Chengwang Guo, Yaohua Deng, Chunfeng Zhang, and Chang Deng "Remaining useful life prediction of bearing based on autoencoder-LSTM", Proc. SPIE 11930, International Conference on Mechanical Engineering, Measurement Control, and Instrumentation, 119300J (14 October 2021); https://doi.org/10.1117/12.2611404

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KEYWORDS

Data modeling

Feature extraction

Computer programming

Network architectures

Process modeling

Electrical engineering

Performance modeling

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