WDBM: Weighted Deep Forest Model Based Bearing Fault Diagnosis Method

作     者：Letao Gao Xiaoming Wang Tao Wang Mengyu Chang 

作者机构：Department of Computer ScienceCity University of Hong KongHong Kong999077China School of Computer and Software EngineerXihua UniversityChengdu610039China Nanjing University of Aeronautics and AstronauticsNanjing210008China McGill UniversityMontrealH3G 1Y2Canada 

出 版 物：《Computers, Materials & Continua》 (计算机、材料和连续体（英文）)

年 卷 期：2022年第72卷第9期

页      面：4741-4754页

核心收录：

学科分类：07[理学] 0701[理学-数学] 070101[理学-基础数学] 

基　　金：:The work is supported by the National Key R&D Program of China(No.2021YFB2700500 2021YFB2700503).Tao Wang received the grant and the URLs to sponsors’websites is https://service.most.gov.cn/ 

主　　题：Deep forest bearing fault diagnosis weights 

摘      要：In the research field of bearing fault diagnosis,classical deep learning models have the problems of too many parameters and high computing *** addition,the classical deep learning models are not effective in the scenario of small *** recent years,deep forest is proposed,which has less hyper parameters and adaptive depth of deep *** addition,weighted deep forest(WDF)is proposed to further improve deep forest by assigning weights for decisions trees based on the accuracy of each decision *** this paper,weighted deep forest model-based bearing fault diagnosis method(WDBM)is *** WDBM is regard as a novel bearing fault diagnosis method,which not only inherits the WDF’s advantages-strong robustness,good generalization,less parameters,faster convergence speed and so on,but also realizes effective diagnosis with high precision and low cost under the condition of small *** verify the performance of the WDBM,experiments are carried out on Case Western Reserve University bearing data set(CWRU).Experiments results demonstrate that WDBM can achieve comparative recognition accuracy,with less computational overhead and faster convergence speed.