Exploration of equipment fault prediction methods for automated production lines
自动化生产线设备故障预测方法探讨

摘要

自动化生产线的高效运作是现代制造业的重要组成部分，而设备故障的预测与预防对于提升生产效率至关重要。本文探讨了自动化生产线设备故障预测的方法，重点分析了数据采集、特征提取、建模及优化算法等关键环节。通过对常见故障的分析，结合机器学习和数据分析技术，提出了基于故障模式识别的预测方法。本文还讨论了故障预测模型的实际应用，及其在生产线维护与优化中的作用。准确的故障预测能够显著降低设备停机时间，提高生产线的稳定性与效益。

Abstract

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.