A novel data‐driven methodology for fault detection and dynamic risk assessment-Reference-Cited by-同舟云学术

A novel data‐driven methodology for fault detection and dynamic risk assessment

Published:2020-05-05 Issue:11 Volume:98 Page:2397-2416
ISSN:0008-4034
Container-title:The Canadian Journal of Chemical Engineering
language:en
Short-container-title:Can J Chem Eng

Author:

Amin Md. Tanjin¹,Khan Faisal¹,Ahmed Salim¹,Imtiaz Syed¹

Affiliation:

1. Centre for Risk, Integrity and Safety Engineering (C‐RISE), Faculty of Engineering & Applied Science Memorial University St. John's Newfoundland and Labrador Canada

Funder

Canada Research Chairs

Natural Sciences and Engineering Research Council of Canada

Publisher

Wiley

Subject

General Chemical Engineering

Link

https://onlinelibrary.wiley.com/doi/pdf/10.1002/cjce.23760

Reference45 articles.

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

2. Multiscale PCA with application to multivariate statistical process monitoring

3. Statistical process monitoring with independent component analysis

4. Process monitoring and diagnosis by multiblock PLS methods

5. Multimode process monitoring with Bayesian inference-based finite Gaussian mixture models

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