An Intelligent Fault Diagnosis Method of Multi-Scale Deep Feature Fusion Based on Information Entropy

作     者：Zhiwu Shang Wanxiang Li Maosheng Gao Xia Liu Yan Yu Zhiwu Shang;Wanxiang Li;Maosheng Gao;Xia Liu;Yan Yu

作者机构：School of Mechanical EngineeringTiangong UniversityTianjin 300387China Tianjin Modern Electromechanical Equipment Technology Key LaboratoryTianjin 300387China 

出 版 物：《Chinese Journal of Mechanical Engineering》 (中国机械工程学报（英文版）)

年 卷 期：2021年第34卷第4期

页      面：121-136页

核心收录：

学科分类：12[管理学] 1201[管理学-管理科学与工程(可授管理学、工学学位)] 081104[工学-模式识别与智能系统] 08[工学] 080401[工学-精密仪器及机械] 0804[工学-仪器科学与技术] 080402[工学-测试计量技术及仪器] 0838[工学-公安技术] 0835[工学-软件工程] 0811[工学-控制科学与工程] 0812[工学-计算机科学与技术（可授工学、理学学位）] 

基　　金：Supported by National Natural Science Foundation of China and Civil Aviation Administration of China Joint Funded Project(Grant No.U1733108) Key Project of Tianjin Science and Technology Support Program(Grant No.16YFZCSY00860) 

主　　题：Fault diagnosis Feature fusion Information entropy Deep autoencoder Deep belief network 

摘      要：For a single-structure deep learning fault diagnosis model,its disadvantages are an insufficient feature extraction and weak fault classification *** paper proposes a multi-scale deep feature fusion intelligent fault diagnosis method based on information ***,a normal autoencoder,denoising autoencoder,sparse autoencoder,and contractive autoencoder are used in parallel to construct a multi-scale deep neural network feature extraction structure.A deep feature fusion strategy based on information entropy is proposed to obtain low-dimensional features and ensure the robustness of the model and the quality of deep ***,the advantage of the deep belief network probability model is used as the fault classifier to identify the *** effectiveness of the proposed method was verified by a gearbox *** results show that,compared with traditional and existing intelligent fault diagnosis methods,the proposed method can obtain representative information and features from the raw data with higher classification accuracy.