Fault diagnosis model of automatic welder for Marine manufacturing

Hang Ye, Qian Yang, Jiping Lu

Research output: Chapter in Book/Report/Conference proceedingConference contributionpeer-review

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.

Original languageEnglish
Title of host publicationInternational Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2023
EditorsSamir Ladaci, Suresh Kaswan
PublisherSPIE
ISBN (Electronic)9781510667662
DOIs
Publication statusPublished - 2023
Event2023 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2023 - Xiamen, China
Duration: 28 Apr 202330 Apr 2023

Publication series

NameProceedings of SPIE - The International Society for Optical Engineering
Volume12759
ISSN (Print)0277-786X
ISSN (Electronic)1996-756X

Conference

Conference2023 International Conference on Automation Control, Algorithm, and Intelligent Bionics, ACAIB 2023
Country/TerritoryChina
CityXiamen
Period28/04/2330/04/23

Keywords

  • Fault diagnosis model
  • Hidden Markov model
  • Welder fault
  • shipbuilding

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