DPLS-MSOM Modeling for Visual Industrial Fault Diagnosis and Monitoring Based on Variation Data from Normal to Anomalous-Reference-Cited by-同舟云学术

DPLS-MSOM Modeling for Visual Industrial Fault Diagnosis and Monitoring Based on Variation Data from Normal to Anomalous

Published:2017-11-30 Issue:49 Volume:56 Page:14518-14529
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Tang Jiawei¹,Yan Xuefeng¹^ORCID

Affiliation:

1. Key Laboratory of Advanced Control and Optimization for Chemical Processes of Ministry of Education, East China University of Science and Technology, Shanghai 200237, People’s Republic of China

Publisher

American Chemical Society (ACS)

Subject

Industrial and Manufacturing Engineering,General Chemical Engineering,General Chemistry

Link

https://pubs.acs.org/doi/pdf/10.1021/acs.iecr.7b02590

Reference30 articles.

1. Risk-based fault diagnosis and safety management for process systems

2. Dynamic risk assessment and fault detection using a multivariate technique

3. Semi-supervised change detection using modified self-organizing feature map neural network

4. Visualisation of plant disturbances using self-organising maps

5. Self-organizing map and clustering for wastewater treatment monitoring

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