Data-driven predictive maintenance method for digital welding machines

Materia

article

Xing-chen Li¹  Dao-fang Chang¹  You-gang SUN² 

[1] Shanghai Maritime University;Tongji University

关键词: Welding machine;    Predictive maintenance;    Lifespan prediction;    Long and short-term memory networks;    Attention mechanism;   

DOI  :  10.1590/1517-7076-RMAT-2023-0096

学科分类：工程和技术（综合）

来源: Universidade Federal do Rio de Janeiro * Coordenacao dos Programas de Pos-Graduacao de Engenharia

【 摘 要 】

Digital welding machine (DWM) is an advanced tool for material forming. The lifespan and health status of DWMs are closely related to the safety and reliability. To address the problem of low accuracy in the lifespan prediction of DWMs, a model based on immune algorithm (IA) and long short-term memory network (LSTM) with attention mechanism is proposed. First, the degradation characteristic indicators of the lifespan of DWMs are evaluated and selected. Then, a health index is constructed using linear regression to quantitatively reflect the lifespan status of DWMs. The optimized model is used to predict the remaining lifespan, and compared with various models using 5 indicators. Finally, predictive maintenance of DWMs is carried out based on product inspection and production scheduling. the optimal solution for the objective function is obtained to calculate the best predictive maintenance method for the digital welding machine.During the lifespan prediction process, the optimized model has a 20% decrease in root mean square error and a 35.8% decrease in mean square error compared to the traditional LSTM model. The average absolute error is decreased by 14.2% and the average absolute percentage error is closer to 0, while the coefficient of determination increases by 23%. By combining with actual production line arrangements, maintenance of DWMs can be performed at the most appropriate time to minimize maintenance costs.

【 授权许可】

CC BY