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primary topic: Multiple Fault Diagnosis in Distillation Column Using Multikernel Support Vector Machine

Multiple Fault Diagnosis in Distillation Column Using Multikernel Support Vector Machine
type: AcademicArticle
type: Article
abstract: Fault detection and diagnosis (FDD) in process industries is an important task for efficient process monitoring and plant safety. It is also essential for improving product quality and reducing production cost by reducing process downtime. Real-time multiscale classification of faults plays a vital role in process monitoring. However, some major issues such as high correlation, complexity, and nonlinearity of data are yet to be addressed. In this paper, a fault diagnosis approach based on multikernel support vector machines is proposed to classify the internal and external faults such as reflux failure, change in reboiler duty, column tray upsets, and change in feed composition, flow, and temperature in a distillation column. The data set is generated using Aspen plus dynamics simulation at normal and faulty states. The classification has been done by various methods such as decision tree, K-nearest neighbors, linear discriminant analysis, artificial neural network, subspace discriminant, and multikernel support vector machine. It is observed that the SVM based diagnostic system provides more accurate root cause isolation. The proposed MK-SVM method was evaluated by using the confusion matrix as the performance evaluator. The result showed that the proposed model has a high FDR which is 99.77 and a very low FAR, i.e., 0.23. © 2018 American Chemical Society.
date: 2018
volume: 57
issue: 43
is part of: Industrial and Engineering Chemistry Research
status: peerReviewed
status: published
author list: authors
type: EPrint
type: ArticleEPrint
is part of: repository

American Chemical Society
type: Organization

Industrial and Engineering Chemistry Research
type: Collection
issn: 08885885

F. Uddin
type: Person
given name: F.
family name: Uddin

H. Zabiri
type: Person
given name: H.
family name: Zabiri

A.S. Maulud
type: Person
given name: A.S.
family name: Maulud

L.D. Tufa
type: Person
given name: L.D.
family name: Tufa

S.A. Taqvi
type: Person
given name: S.A.
family name: Taqvi

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primary topic: Multiple Fault Diagnosis in Distillation Column Using Multikernel Support Vector Machine