Intelligent fault diagnosis of rotating machinery based on deep learning with feature selection

作     者：Han, Dongying Liang, Kai Shi, Peiming 

作者机构：Yanshan Univ Sch Vehicles & Energy Qinhuangdao Hebei Peoples R China Yanshan Univ Sch Elect Engn Qinhuangdao 006004 Hebei Peoples R China 

出 版 物：《JOURNAL OF LOW FREQUENCY NOISE VIBRATION AND ACTIVE CONTROL》 (J. Low Freq. Noise Vib. Act. Control)

年 卷 期：2020年第39卷第4期

页      面：939-953页

核心收录：

学科分类：07[理学] 082403[工学-水声工程] 08[工学] 070206[理学-声学] 0824[工学-船舶与海洋工程] 0702[理学-物理学] 

基　　金：The author(s) disclosed receipt of the following financial support for the research  authorship  and/or publication of this article: This work was supported by the National Natural Science Foundation of China (Grant No. 51475407  51875500)  Natural Science Foundation of Hebei Province of China No. E2015203190  and Key project of Natural Science Research in Colleges and Universities of Hebei Province of China No. ZD2015050 

主　　题：Intelligent fault diagnosis empirical mode decomposition feature selection denoising auto-encoder deep learning 

摘      要：In the absence of a priori knowledge, manual feature selection is too blind to find the sensitive features which can effectively classify the different fault features. And it is difficult to obtain a large number of typical fault samples in practice to train the intelligent classifier. A novel intelligent fault diagnosis method based on feature selection and deep learning is proposed for rotating machine mechanical in the paper. In this method, the deep neural network is not only used for feature extraction but also for fault diagnosis. First, the deep neural network 1 is used to extract feature from the spectral signal of the original signal. In addition, the original vibration signal is decomposed to a series of intrinsic mode function components by empirical mode decomposition, and the statistical features of each intrinsic mode function component are extracted by the deep neural network 2 in time domain and frequency domain. Second, the extraction features of the original signal spectrum and the extraction features of each intrinsic mode function component are evaluated, respectively. After features evaluation, the selected sensitive features are combined together to construct a joint feature. Finally, the joint feature is put into the deep neural network 3 to realize the automatic recognition of different fault states of rotating machinery. The experimental results show that the method proposed in this paper which integrated time-domain, frequency-domain statistical characteristics, empirical mode decomposition, feature selection, and deep learning methods can obtain the fault information in detail and can select sensitive features from a large number of fault features. The method can reduce the network size, improve the mechanical fault diagnosis classification accuracy, and has strong robustness.