Multi-channel data fusion and intelligent fault diagnosis based on deep learning. (1st January 2023)

• Record Type: Journal Article
• Authors: Guo, Yiming; Hu, Tao; Zhou, Yifan; Zhao, Kunkun; Zhang, Zhisheng
• Is Part Of: Measurement science & technology. Volume 34:Number 1(2023)
• Publication Date: 2023-01-01
• Subjects: multi-channel data -- fault diagnosis -- convolutional neural network -- two-scale feature extraction; Physical measurements -- Periodicals; Scientific apparatus and instruments -- Periodicals; Equipment and Supplies -- Periodicals; Science -- instrumentation -- Periodicals; Technology -- instrumentation -- Periodicals; Mesures physiques -- Périodiques; Physical measurements; Scientific apparatus and instruments; Periodicals; 502.87
• DOI: 10.1088/1361-6501/ac8a64
• Languages: English
• ISSNs: 0957-0233

Abstract

Abstract: In complex manufacturing systems, multi-channel sensor data are usually recorded for fault detection and diagnosis. Most existing multi-channel data processing methods adopt tensor analysis technology, which cannot effectively describe the temporal and spatial structures of the multi-channel data. The obstacles in multi-channel data analysis are the temporal correlation between the time-series data of the single-channel and the spatial correlation between different channels. In this paper, a novel deep convolutional neural network model is proposed for multi-channel data fusion and intelligent fault diagnosis. First, features of the multi-channel data are extracted from two scales. The extracted features are then fused through a multi-layer neural network. Finally, a classifier of fault modes is established by using the improved Softmax function. The fault diagnosis performance of the proposed model is evaluated and compared with other common methods in both the simulation studies and real-world case studies. Results show that the proposed methodology has superior fault diagnosis performance for multi-channel data.