Research on a Fault Diagnosis Method of an A-Class Thermal Insulation Panel Production Line Based on Multi-Sensor Data Fusion

Abstract

Featured Application: Experimental results show that for the test dataset composed of A-class insulation board production line sensor data, the proposed method achieves better estimation results compared with a basic LSTM algorithm; its performance in each evaluation index is better. To detect the running state of an A-class thermal insulation board production line in real time, conveniently and accurately, a fault diagnosis method based on multi-sensor data fusion was proposed. The proposed algorithm integrates the ideas of Convolutional Neural Network (CNN), Long Short-Term Memory (LSTM) and Attention Mechanism, and combines a Dilated Convolution Module (DCM) with LSTM to recognize complex signals of multiple sensors. By introducing an attention mechanism, the recognition performance of the network was improved. Finally, the real-time status information of the production line was obtained by integrating attention weight. Experimental results show that for the custom multi-sensor dataset of A-class insulation board production line, the proposed CNN-LSTM fault diagnosis method achieved 98.97% accuracy. Compared with other popular algorithms, the performance of the proposed CNN-LSTM model performed excellently in each evaluation index is better.