A Deep Learning Approach for Industrial Equipment Fault Detection: Integrating Convolutional and Long Short-Term Memory Networks

Yu Sun

Abstract: The reliability of industrial equipment is crucial for the normal operation of production lines. Traditional fault detection methods rely on expert experience and struggle to adapt to complex and variable industrial environments. This paper proposes a fault detection method based on deep learning, which analyzes and models the operational data of industrial equipment using Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks to automatically identify potential faults. Experimental results show that this method outperforms traditional methods in terms of accuracy and real-time performance, providing an effective solution for industrial fault detection.

Submission Number: 230