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A probabilistic multivariate method for fault diagnosis of industrial processes
Citation
Yu, H and Khan, F and Garaniya, V, A probabilistic multivariate method for fault diagnosis of industrial processes, Chemical Engineering Research and Design, 104 pp. 306-318. ISSN 0263-8762 (2015) [Refereed Article]
Copyright Statement
Copyright 2015 The Institution of Chemical Engineers
DOI: doi:10.1016/j.cherd.2015.08.026
Abstract
A probabilistic multivariate fault diagnosis technique is proposed for industrial processes.
The joint probability density function containing essential features of normal operation is
constructed considering dependency among the process variables. The dependence structures
are modelled using Gaussian copula. The Gaussian copula uses rank correlation
coefficients to capture the nonlinear relationships between process variables. For realtime
monitoring, the probability of each online data samples is computed under the joint
probability density function. Those samples having probabilities violating a predetermined
control limit are classified to be faulty. For fault diagnosis, the reference dependence structures
ofthe process variables are first determined from normal process data. These reference
structures are then compared with those obtained from the faulty data samples. This assists
in identifying the root-cause variable(s). The proposed technique is tested on two case studies:
a nonlinear numerical example and an industrial case. The performance of the proposed
technique is observed to be superior to the conventional statistical methods, such as PCA
and MICA.
Item Details
Item Type: | Refereed Article |
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Keywords: | Fault detection, Fault diagnosis, Nonlinear processes, Multivariate Gaussian copula |
Research Division: | Engineering |
Research Group: | Electronics, sensors and digital hardware |
Research Field: | Industrial electronics |
Objective Division: | Manufacturing |
Objective Group: | Instrumentation |
Objective Field: | Industrial instruments |
UTAS Author: | Yu, H (Mr Hongyang Yu) |
UTAS Author: | Garaniya, V (Associate Professor Vikram Garaniya) |
ID Code: | 103187 |
Year Published: | 2015 |
Web of Science® Times Cited: | 25 |
Deposited By: | NC Maritime Engineering and Hydrodynamics |
Deposited On: | 2015-09-24 |
Last Modified: | 2017-11-03 |
Downloads: | 0 |
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