Recursive Hybrid Variable Monitoring for Fault Detection in Nonstationary Industrial Processes

Author:

Wang Min1,Zhou Donghua1ORCID,Chen Maoyin1ORCID

Affiliation:

1. Department of Automation, Tsinghua University, Beijing, China

Funder

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

Cited by 17 articles. 订阅此论文施引文献 订阅此论文施引文献,注册后可以免费订阅5篇论文的施引文献,订阅后可以查看论文全部施引文献

1. Incipient Fault Detection with Feature Ensemble Based on One-Class Machine Learning Methods;2023 62nd IEEE Conference on Decision and Control (CDC);2023-12-13

2. Probabilistic stationary subspace regression model for soft sensing of nonstationary industrial processes;The Canadian Journal of Chemical Engineering;2023-11-28

3. Active Fault Diagnosis for Stochastic Systems Within Bayesian Minimum Risk Decision Framework;IEEE Transactions on Industrial Informatics;2023-10

4. A Flexible Probabilistic Framework With Concurrent Analysis of Continuous and Categorical Data for Industrial Fault Detection and Diagnosis;IEEE Transactions on Industrial Informatics;2023-10

5. Active fault-tolerant control for nonlinear systems with actuator faults and mismatched disturbances;2023 CAA Symposium on Fault Detection, Supervision and Safety for Technical Processes (SAFEPROCESS);2023-09-22

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