Fault prediction of coating machine based on a composite neural network

Dongwei Gu; Juncheng Wang; Ruihua Nie; Qihan Li; Zhe Long; Pengfei Chen

doi:10.1117/12.3007526

25 October 2023 Fault prediction of coating machine based on a composite neural network

Dongwei Gu, Juncheng Wang, Ruihua Nie, Qihan Li, Zhe Long, Pengfei Chen

Proceedings Volume 12801, Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023); 1280160 (2023) https://doi.org/10.1117/12.3007526
Event: Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023), 2023, Dalian, China

Abstract

This paper proposes a BP-LSTM fault prediction model, designed to address the challenge of low prediction accuracy in strong nonlinearity equipment sample data in the prediction process. The proposed model combines the strengths of Long Short-Term Memory Network (LSTM) with long-term memory function in time series modeling problems and the powerful generalization capabilities in Back-Propagation (BP) neural networks. To validate the effectiveness of the model, fault data collected from the coating machines in 2018 and 2019 were utilized, the model's validity was analyzed by coating machine prediction of the Fault Moment (FM) and Fault Impact (FI), and compared with 4 models such as LSTM and BP neural network. The analysis results show that the model accuracy is 94.2% in the fault prediction, the optimal Root Mean Squared Error (RMSE) achieves 0.1354. The prediction accuracy and robustness are significantly better than other models. The BP-LSTM fault prediction model proposed in this paper can be effectively extended to complex electromechanical equipment, providing theoretical guidance and a basis for the equipment maintenance strategies development based on the prediction results.

(2023) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Dongwei Gu, Juncheng Wang, Ruihua Nie, Qihan Li, Zhe Long, and Pengfei Chen "Fault prediction of coating machine based on a composite neural network", Proc. SPIE 12801, Ninth International Conference on Mechanical Engineering, Materials, and Automation Technology (MMEAT 2023), 1280160 (25 October 2023); https://doi.org/10.1117/12.3007526

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