Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis

作     者：Jiaxin Ren Jingcheng Wen Zhibin Zhao Ruqiang Yan Xuefeng Chen Asoke K.Nandi Jiaxin Ren;Jingcheng Wen;Zhibin Zhao;Ruqiang Yan;Xuefeng Chen;Asoke K.Nandi

作者机构：School of Mechanical EngineeringXi’an Jiaotong UniversityXi’an 710049China Department of Electronic and Electrical EngineeringBrunel University LondonKingston LaneUxbridgeUB83PHUK IEEE 

出 版 物：《IEEE/CAA Journal of Automatica Sinica》 (自动化学报（英文版）)

年 卷 期：2024年第11卷第6期

页      面：1317-1330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 in part by the National Natural Science Foundation of China(52105116) Science Center for gas turbine project(P2022-DC-I-003-001) the Royal Society award(IEC\NSFC\223294)to Professor Asoke K.Nandi 

主　　题：Out-of-distribution detection traceability analysis trustworthy fault diagnosis uncertainty quantification. 

摘      要：Recently,intelligent fault diagnosis based on deep learning has been extensively investigated,exhibiting state-of-the-art ***,the deep learning model is often not truly trusted by users due to the lack of interpretability of“black box,which limits its deployment in safety-critical applications.A trusted fault diagnosis system requires that the faults can be accurately diagnosed in most cases,and the human in the deci-sion-making loop can be found to deal with the abnormal situa-tion when the models *** this paper,we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks,called *** SAEU,Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural *** on the SAEU,we propose a unified uncertainty-aware deep learning framework(UU-DLF)to realize the grand vision of trustworthy fault ***,our UU-DLF effectively embodies the idea of“humans in the loop,which not only allows for manual intervention in abnor-mal situations of diagnostic models,but also makes correspond-ing improvements on existing models based on traceability ***,two experiments conducted on the gearbox and aero-engine bevel gears are used to demonstrate the effectiveness of UU-DLF and explore the effective reasons behind.