Fault diagnosis model of automatic welder for marine manufacturing

Hang Ye; Qian Yang; Jiping Lu

doi:10.1117/12.2686638

10 August 2023 Fault diagnosis model of automatic welder for marine manufacturing

Hang Ye, Qian Yang, Jiping Lu

Author Affiliations +

Proceedings Volume 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023); 1275908 (2023) https://doi.org/10.1117/12.2686638
Event: 2023 3rd International Conference on Automation Control, Algorithm and Intelligent Bionics (ACAIB 2023), 2023, Xiamen, China

Abstract

In order to reduce the production efficiency of welding machine faults in the process of shipbuilding, a fault diagnosis model of welding machine based on hidden Markov chain is established in this paper. Firstly, based on the historical fault data of the welder, the fault diagnosis model can be used to select the corresponding data segments of fault states such as internal line break, power phase loss and sensor damage. Secondly, the effective data is used to train the hidden Markov model, and the parameters of each fault model are optimized and iterated. Finally, the experimental platform of welding machine fault diagnosis is built, and the welding machine fault diagnosis experiment is carried out. The experimental results show that the model can accurately diagnose welder faults, reduce equipment downtime and improve production efficiency by calculating the matching degree between welder real-time working data and each fault model.

Citation Download Citation

Hang Ye, Qian Yang, and Jiping Lu "Fault diagnosis model of automatic welder for marine manufacturing", Proc. SPIE 12759, International Conference on Automation Control, Algorithm, and Intelligent Bionics (ACAIB 2023), 1275908 (10 August 2023); https://doi.org/10.1117/12.2686638

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