Robust Degradation State Identification in the Presence of Parameter Uncertainty and Outliers-Reference-Cited by-同舟云学术

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Robust Degradation State Identification in the Presence of Parameter Uncertainty and Outliers

Published:2024-02 Issue:2 Volume:20 Page:2644-2652
ISSN:1551-3203
Container-title:IEEE Transactions on Industrial Informatics
language:
Short-container-title:IEEE Trans. Ind. Inf.

Author:

Liu Xingchen¹^ORCID,Wang Xin²^ORCID,Xie Min³^ORCID,Ye Zhisheng⁴^ORCID

Affiliation:

1. Center for Advances in Reliability and Safety, Hong Kong Polytechnic University, Hong Kong

2. Sino-US Global Logistics Institute, Antai College of Economics and Management, Shanghai Jiao Tong University, Shanghai, China

3. Department of Advanced Design and System Engineering, City University of Hong Kong, Hong Kong

4. Department of Industrial Systems Engineering and Management, National University of Singapore, Singapore

Funder

National Natural Science Foundation of China

Research Grant Council of Hong Kong

Hong Kong Innovation and Technology Commission

Future Resilient Systems Project

National Research Foundation Singapore

CREATE programme

Shanghai Pujiang Program

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/ielx7/9424/10411967/10196301.pdf?arnumber=10196301

Reference33 articles.

1. Data-Driven Battery Health Prognosis Using Adaptive Brownian Motion Model

2. A Two-Stage Data-Driven-Based Prognostic Approach for Bearing Degradation Problem

3. Degradation data analysis and remaining useful life estimation: A review on Wiener-process-based methods

4. A Bidirectional LSTM Prognostics Method Under Multiple Operational Conditions

5. Estimation of Bearing Remaining Useful Life Based on Multiscale Convolutional Neural Network

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1. RUL prediction for two-phase degrading systems considering physical damage observations;Reliability Engineering & System Safety;2024-04

2. A hybrid feature selection model for identifying groups of critical elements in aero‐engine assembly;Quality and Reliability Engineering International;2024-02-29

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