Research on fault diagnosis method of industrial automation control system based on deep learning
基于深度学习的工业自动化控制系统故障诊断方法研究

摘要

随着工业自动化程度的不断提升，传统的故障诊断方法逐渐暴露出局限性，尤其在复杂环境中，准确性和实时性要求更高。基于深度学习的工业自动化控制系统故障诊断方法通过利用深度神经网络的强大特性，能够有效提升故障检测和预测的准确性。本研究深入探讨了深度学习在自动化控制系统故障诊断中的应用，分析了常见的网络模型和算法，比较了不同模型在工业故障诊断中的表现，并提出了基于深度学习的多层次诊断框架。深度学习能够极大地优化故障诊断效率，提高工业生产的安全性和稳定性。

Abstract

With the continuous improvement of industrial automation, traditional fault diagnosis methods gradually reveal their limitations. Especially in complex environments, higher requirements are imposed on accuracy and real-time performance. The fault diagnosis method of industrial automation control system based on deep learning can effectively improve the accuracy of fault detection and prediction by utilizing the powerful features of deep neural networks. This study deeply explores the application of deep learning in the fault diagnosis of automation control systems, analyzes common network models and algorithms, compares the performance of different models in industrial fault diagnosis, and proposes a multi-level diagnosis framework based on deep learning. Deep learning can greatly optimize the efficiency of fault diagnosis and improve the safety and stability of industrial production.