A Compact Convolutional Neural Network Augmented with Multiscale Feature Extraction of Acquired Monitoring Data for Mechanical Intelligent Fault Diagnosis. (April 2020)

• Record Type: Journal Article
• Authors: Zhang, Kaiyu; Chen, Jinglong; Zhang, Tianci; Zhou, Zitong
• Is Part Of: Journal of manufacturing systems. Volume 55(2020)
• Page Start: 273
• Page End: 284
• Publication Date: 2020-04
• Subjects: Fault diagnosis -- Convolutional neural network -- Small sample -- Multiscale feature extraction; Manufacturing processes -- Periodicals; Production engineering -- Data processing -- Periodicals; Robots, Industrial -- Periodicals; Production, Technique de la -- Informatique -- Périodiques; Robots industriels -- Périodiques; Electronic journals; 670.42
• DOI: 10.1016/j.jmsy.2020.04.016
• Languages: English
• ISSNs: 0278-6125

Abstract

Highlights: A novel convolutional neural network is presented for small sample fault diagnosis. Multiscale feature extraction unit is developed to extract multiscale features by using one layer of convolution. A specially designed compact convolutional neural network is introduced to synthetically analyze the multiscale features. The validity and feasibility of proposed method for small sample fault diagnosis is demonstrated by two bearing data sets. Abstract: Considering all the monitoring data of bearings until failure, very few data are acquired when the bearings are faulty. Such circumstance leads to small faulty sample problem when an intelligent fault diagnosis method is applied. A deep neural network trained with small samples cannot be trained completely, and tends to overfit, which results in poor performance in practical application. To solve this problem, a compact convolutional neural network augmented with multiscale feature extraction is proposed in this paper. Multiscale feature extraction unit is introduced to extract features at different time scales without adding convolution layers, which can reduce the depth of the network while ensuring classification ability and alleviating the overfitting problem caused by the network being too complicated. Besides, a specially designed compact convolutional neural network synthetically analyzes the multiscale features. By combing these two tricks, the proposed neural network can extract more sensitive features with a relatively shallow structure, which increases classification accuracy under small samples. Dropout technique is also used to prevent the network from overfitting. Effectiveness of the proposed method is verified by three bearing datasets. Experiments show that this network can achieve competitive results with limited training samples even with different load and mixed rotating speed.