Trustworthy Fault Diagnosis With Uncertainty Estimation Through Evidential Convolutional Neural Networks-Reference-Cited by-同舟云学术

Trustworthy Fault Diagnosis With Uncertainty Estimation Through Evidential Convolutional Neural Networks

Published:2023-11 Issue:11 Volume:19 Page:10842-10852
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Zhou Hanting¹^ORCID,Chen Wenhe¹^ORCID,Cheng Longsheng¹^ORCID,Liu Jing²,Xia Min³^ORCID

Affiliation:

1. School of Economics and Management, Nanjing University of Science and Technology, Nanjing, China

2. School of Marine Science and Technology, Northwestern Polytechnical University, Xi'an, China

3. School of Engineering, Lancaster University, Lancaster, U.K.

Funder

China Scholarship Council

Royal Society

National Natural Science Foundation of China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

Electrical and Electronic Engineering,Computer Science Applications,Information Systems,Control and Systems Engineering

Link

http://xplorestaging.ieee.org/ielx7/9424/10255543/10035450.pdf?arnumber=10035450

Reference35 articles.

1. A probabilistic Bayesian recurrent neural network for remaining useful life prognostics considering epistemic and aleatory uncertainties

2. A Multisource Dense Adaptation Adversarial Network for Fault Diagnosis of Machinery

3. Out-of-distribution detection-assisted trustworthy machinery fault diagnosis approach with uncertainty-aware deep ensembles

4. A new structured domain adversarial neural network for transfer fault diagnosis of rolling bearings under different working conditions;mao;IEEE Trans Instrum Meas,2020

5. Data-Driven Mode Identification and Unsupervised Fault Detection for Nonlinear Multimode Processes

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