Journal of Electrical Engineering and Automation

The efficient operation of automated production lines is a crucial component of modern manufacturing, and the prediction and prevention of equipment faults are vital for improving production efficiency. This paper explores the methods for predicting equipment faults in automated production lines, with a focus on analyzing key links such as data collection, feature extraction, modeling, and optimization algorithms. Through the analysis of common faults, combined with machine learning and data analysis technologies, a prediction method based on fault pattern recognition is proposed. The paper also discusses the practical application of fault prediction models and their role in the maintenance and optimization of production lines. Accurate fault prediction can significantly reduce equipment downtime and improve the stability and efficiency of production lines.

Automated production line; Equipment fault prediction; Machine learning; Fault pattern recognition; Data analysis