Dynamic Failure Analysis of Process Systems Using Principal Component Analysis and Bayesian Network-Reference-Cited by-同舟云学术

Dynamic Failure Analysis of Process Systems Using Principal Component Analysis and Bayesian Network

Published:2017-02-16 Issue:8 Volume:56 Page:2094-2106
ISSN:0888-5885
Container-title:Industrial & Engineering Chemistry Research
language:en
Short-container-title:Ind. Eng. Chem. Res.

Author:

Adedigba Sunday A.¹,Khan Faisal¹^ORCID,Yang Ming¹

Affiliation:

1. Centre for Risk, Integrity and Safety Engineering (C-RISE), Faculty of Engineering and Applied Science, Memorial University of Newfoundland, St. John’s, Newfoundland, Canada A1B 3X5

Funder

Canada Research Chairs

Natural Sciences and Engineering Research Council of Canada

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.6b03356

Reference52 articles.

1. Modified Independent Component Analysis and Bayesian Network-Based Two-Stage Fault Diagnosis of Process Operations

2. Nonlinear Gaussian Belief Network based fault diagnosis for industrial processes

3. Risk-based fault detection using Self-Organizing Map

4. A review of process fault detection and diagnosis

5. Consistent dynamic PCA based on errors-in-variables subspace identification

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