Gear fault detection method based on improved PSO and LSTM

Chen Zhang; Chongqing Zhang; Ya Shen

doi:10.1117/12.2679697

18 July 2023 Gear fault detection method based on improved PSO and LSTM

Chen Zhang, Chongqing Zhang, Ya Shen

Proceedings Volume 12722, Third International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023); 1272230 (2023) https://doi.org/10.1117/12.2679697
Event: International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023), 2023, Hangzhou, China

Abstract

Gear is an important part of power transmission between machines. Long Term and short term memory network (LSTM) is adopted as the basic model for gear fault detection. Aiming at the problem that the model hyperparameters of LSTM are difficult to be determined manually, by using the improved particle swarm optimization (IPSO) to find the optimal LSTM superparameters automatically. Optimize the learning factor and inertia weight in PSO to enhance particle optimization ability, so as to build IPSO-LSTM model with better detection ability. In this paper, the gear fault data set published by the University of Connecticut was used to verify the detection model, and the fault detection accuracy of the gear fault data set reached 98.7%. Compared with the unimproved PSO-LSTM model, LSTM and CNN, the accuracy rate is increased by 2.9%,7.4% and 10% respectively.

Citation Download Citation

Chen Zhang, Chongqing Zhang, and Ya Shen "Gear fault detection method based on improved PSO and LSTM", Proc. SPIE 12722, Third International Conference on Mechanical, Electronics, and Electrical and Automation Control (METMS 2023), 1272230 (18 July 2023); https://doi.org/10.1117/12.2679697

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