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A Hybrid Process Monitoring and Fault Diagnosis Approach for Chemical Plants
Joint Authors
Source
International Journal of Chemical Engineering
Issue
Vol. 2015, Issue 2015 (31 Dec. 2015), pp.1-9, 9 p.
Publisher
Hindawi Publishing Corporation
Publication Date
2015-11-04
Country of Publication
Egypt
No. of Pages
9
Abstract EN
Given their potentially enormous risk, process monitoring and fault diagnosis for chemical plants have recently been the focus of many studies.
Based on hazard and operability (HAZOP) analysis, kernel principal component analysis (KPCA), wavelet neural network (WNN), and fault tree analysis (FTA), a hybrid process monitoring and fault diagnosis approach is proposed in this study.
HAZOP analysis helps identify the fault modes and determine process variables monitored.
The KPCA model is then constructed to reduce monitoring variable dimensionality.
Meanwhile, the fault features of the monitoring variables are extracted, so then process monitoring can be performed with the squared prediction error (SPE) statistics of KPCA.
Then, multiple WNN models are designed through the use of low-dimensional sample data preprocessed by KPCA as the training and test samples to detect the fault mode online.
Finally, FTA approach is introduced to further locate the fault root causes of the fault mode.
The proposed approach is applied to process monitoring and fault diagnosis in a depropanizer unit.
Case study results indicate that this approach can be applicable to process monitoring and diagnosis in large-scale chemical plants.
Accordingly, the approach can serve as an early and reliable basis for technicians’ and operators’ safety management decision-making.
American Psychological Association (APA)
Guo, Lijie& Kang, Jianxin. 2015. A Hybrid Process Monitoring and Fault Diagnosis Approach for Chemical Plants. International Journal of Chemical Engineering،Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1065402
Modern Language Association (MLA)
Guo, Lijie& Kang, Jianxin. A Hybrid Process Monitoring and Fault Diagnosis Approach for Chemical Plants. International Journal of Chemical Engineering No. 2015 (2015), pp.1-9.
https://search.emarefa.net/detail/BIM-1065402
American Medical Association (AMA)
Guo, Lijie& Kang, Jianxin. A Hybrid Process Monitoring and Fault Diagnosis Approach for Chemical Plants. International Journal of Chemical Engineering. 2015. Vol. 2015, no. 2015, pp.1-9.
https://search.emarefa.net/detail/BIM-1065402
Data Type
Journal Articles
Language
English
Notes
Includes bibliographical references
Record ID
BIM-1065402