An adaptive deep convolutional neural network for rolling bearing fault diagnosis

Wang Fuan, Jiang Hongkai, Shao Haidong, Duan Wenjing, Wu Shuaipeng

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204 引用 (Scopus)

摘要

The working conditions of rolling bearings usually is very complex, which makes it difficult to diagnose rolling bearing faults. In this paper, a novel method called the adaptive deep convolutional neural network (CNN) is proposed for rolling bearing fault diagnosis. Firstly, to get rid of manual feature extraction, the deep CNN model is initialized for automatic feature learning. Secondly, to adapt to different signal characteristics, the main parameters of the deep CNN model are determined with a particle swarm optimization method. Thirdly, to evaluate the feature learning ability of the proposed method, t-distributed stochastic neighbor embedding (t-SNE) is further adopted to visualize the hierarchical feature learning process. The proposed method is applied to diagnose rolling bearing faults, and the results confirm that the proposed method is more effective and robust than other intelligent methods.

源语言英语
文章编号095005
期刊Measurement Science and Technology
28
9
DOI
出版状态已出版 - 16 8月 2017

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