Modeling of Predictive Maintenance Systems for Laser-Welders in Continuous Galvanizing Lines Based on Machine Learning with Welder Control Data

• Published in: Sustainability 2023-05, Vol.15 (9), p.7676
• Main Authors: Choi, Jin-Seong, Choi, So-Won, Lee, Eul-Bum
• Format: Article
• Language: English
• Subjects: Abnormalities; Algorithms; Artificial intelligence; Cold; Continuous casting; Control data (computers); Corrosion resistance; Failure; Galvanizing; Hot rolling; Innovations; Intermetallic compounds; Iron compounds; Laser beam welding; Lasers; Learning algorithms; Long short-term memory; Machine learning; Manufacturing; Metal workers; Predictive maintenance; Preventive maintenance; Steel industry; Steel production; Steel products; Steel-works; Sustainability; Welding machines
• ISSN: 2071-1050; 2071-1050
• DOI: 10.3390/su15097676

This study aimed to develop a predictive maintenance model using machine learning (ML) techniques to automatically detect equipment failures before line shutdowns due to equipment malfunctions, explicitly focusing on laser welders in the continuous galvanizing lines (CGLs) of a steel plant in Korea. The study selected an auto-encoder (AE) as a base model, which has the strength of applying normal data and a long short-term memory (LSTM) model for application to time series data, such as equipment operation data. Here, a laser welder predictive maintenance model (LW-PMM) based on the LSTM-AE algorithm was developed by combining the technical advantages of both algorithms. Approximately 1500 types of data were collected, and approximately 200 were selected through preprocessing. The training and testing datasets were split at a ratio of 8:2, and the model parameters were optimized using 10-fold cross-validation. The performance evaluation of the LW-PMM resulted in an accuracy rate of 97.3%, a precision rate of 79.8%, a recall rate of 100%, and an F1-score of 88.8%. The precision of 79.8% compared to the 100% recall value indicated that although the model predicted all failures in the equipment as failures, 20.2% of them were duplicate values, which can be interpreted as one of the five failure signals being not an actual failure. As a result of the application to an actual CGL operation site, equipment abnormalities were detected for the first time 27 h before failure, resulting in a reduction of 18 h compared with the existing process. This study is unique because it started as a proof of concept (POC) and was validated in a production setting as a pilot system for the predictive maintenance of laser welders. We expect this study to be expanded and applied to steel production processes, contributing to digital transformation and innovation in the steel industry.