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 

机构地区：[1]School of Mechanical Engineering,Xi’an Jiaotong University,Xi’an 710049,China [2]Department of Electronic and Electrical Engineering,Brunel University London,Kingston Lane,Uxbridge,UB83PH,UK [3]IEEE

出　　处：《IEEE/CAA Journal of Automatica Sinica》2024年第6期1317-1330,共14页自动化学报（英文版）

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.

摘　　要：Recently,intelligent fault diagnosis based on deep learning has been extensively investigated,exhibiting state-of-the-art performance.However,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 fail.In this paper,we explore a simplified method for quantifying both aleatoric and epistemic uncertainty in deterministic networks,called SAEU.In SAEU,Multivariate Gaussian distribution is employed in the deep architecture to compensate for the shortcomings of complexity and applicability of Bayesian neural networks.Based on the SAEU,we propose a unified uncertainty-aware deep learning framework(UU-DLF)to realize the grand vision of trustworthy fault diagnosis.Moreover,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 analy-sis.Finally,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.

关 键 词：Out-of-distribution detection traceability analysis trustworthy fault diagnosis uncertainty quantification. 

分 类 号：TP18[自动化与计算机技术—控制理论与控制工程] TP277[自动化与计算机技术—控制科学与工程]