A Novel Method for Intelligent Fault Diagnosis of Bearing Based on Capsule Neural Network. (20th June 2019)

• Record Type: Journal Article
• Authors: Wang, Zhijian; Zheng, Likang; Du, Wenhua; Cai, Wenan; Zhou, Jie; Wang, Jingtai; Han, Xiaofeng; He, Gaofeng
• Other Names: Amancio Diego R. Academic Editor.
• Is Part Of: Complexity. Volume 2019(2019)
• Publication Date: 2019-06-20
• Subjects: Chaotic behavior in systems -- Periodicals; Complexity (Philosophy) -- Periodicals; 003
• DOI: 10.1155/2019/6943234
• Languages: English
• ISSNs: 1076-2787

Abstract

Abstract : In the era of big data, data-driven methods mainly based on deep learning have been widely used in the field of intelligent fault diagnosis. Traditional neural networks tend to be more subjective when classifying fault time-frequency graphs, such as pooling layer, and ignore the location relationship of features. The newly proposed neural network named capsules network takes into account the size and location of the image. Inspired by this, capsules network combined with the Xception module (XCN) is applied in intelligent fault diagnosis, so as to improve the classification accuracy of intelligent fault diagnosis. Firstly, the fault time-frequency graphs are obtained by wavelet time-frequency analysis. Then the time-frequency graphs data which are adjusted the pixel size are input into XCN for training. In order to accelerate the learning rate, the parameters which have bigger change are punished by cost function in the process of training. After the operation of dynamic routing, the length of the capsule is used to classify the types of faults and get the classification of loss. Then the longest capsule is used to reconstruct fault time-frequency graphs which are used to measure the reconstruction of loss. In order to determine the convergence condition, the three losses are combined through the weight coefficient. Finally, the proposed model and the traditional methods are, respectively, trained and tested under laboratory conditions and actual wind turbine gearbox conditions to verify the classification ability and reliable ability.