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

Jiaxin Ren Jingcheng Wen Zhibin Zhao Ruqiang Yan Xuefeng Chen Asoke K.Nandi

IEEE/CAA Journal of Automatica Sinica2024，Vol.11Issue(6)：P.1317-1330,14.

IEEE/CAA Journal of Automatica Sinica2024，Vol.11Issue(6)：P.1317-1330,14.DOI:10.1109/JAS.2024.124290

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

Jiaxin Ren ¹Jingcheng Wen ¹Zhibin Zhao ²Ruqiang Yan ²Xuefeng Chen ²Asoke K.Nandi³

作者信息

1. School of Mechanical Engineering,Xi’an Jiaotong University,Xi’an 710049,China
2. School of Mechanical Engineering,Xi’an Jiaotong University,Xi’an 710049,China IEEE
3. Department of Electronic and Electrical Engineering,Brunel University London,Kingston Lane,Uxbridge,UB83PH,UK IEEE
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摘要

关键词

Out-of-distribution detection/traceability analysis/trustworthy fault diagnosis/uncertainty quantification.

分类

计算机与自动化

引用本文复制引用

Jiaxin Ren,Jingcheng Wen,Zhibin Zhao,Ruqiang Yan,Xuefeng Chen,Asoke K.Nandi..Uncertainty-Aware Deep Learning: A Promising Tool for Trustworthy Fault Diagnosis[J].IEEE/CAA Journal of Automatica Sinica,2024,11(6):P.1317-1330,14.

基金项目

supported in part by the National Natural Science Foundation of China(52105116) （52105116）

Science Center for gas turbine project(P2022-DC-I-003-001) （P2022-DC-I-003-001）

the Royal Society award(IEC\NSFC\223294)to Professor Asoke K.Nandi. （IEC\NSFC\223294）

IEEE/CAA Journal of Automatica Sinica

OACSTPCDEI

ISSN：2329-9266

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