Autoencoder embedded dictionary learning for nonlinear industrial process fault diagnosis-Reference-Cited by-同舟云学术

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Autoencoder embedded dictionary learning for nonlinear industrial process fault diagnosis

Published:2021-05 Issue: Volume:101 Page:24-34
ISSN:0959-1524
Container-title:Journal of Process Control
language:en
Short-container-title:Journal of Process Control

Author:

Li Yanxia,Chai Yi,Yin Hongpeng

Funder

Graduate School, Chongqing University

National Natural Science Foundation of China

Fundamental Research Funds for the Central Universities

Publisher

Elsevier BV

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Modeling and Simulation,Control and Systems Engineering

Reference54 articles.

1. A novel hierarchical detection and isolation framework for quality-related multiple faults in large-scale processes;Ma;IEEE Trans. Ind. Electron.,2020

2. Incipient sensor fault diagnosis in multimode processes using conditionally independent bayesian learning based recursive transformed component statistical analysis;Shang;J. Process Control,2019

3. A review of data-driven fault detection and diagnosis methods: Applications in chemical process systems;Nor;Rev. Chem. Eng.,2020

4. A comparison study of basic data-driven fault diagnosis and process monitoring methods on the benchmark tennessee eastman process;Yin;J. Process Control,2012

5. A bibliometric review and analysis of data-driven fault detection and diagnosis methods for process systems;Alauddin;Ind. Eng. Chem. Res.,2018

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