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Modified independent component analysis and Bayesian network-based two-stage fault diagnosis of process operations
Citation
Yu, H and Khan, F and Garaniya, V, Modified independent component analysis and Bayesian network-based two-stage fault diagnosis of process operations, Industrial and Engineering Chemistry Research, 54, (10) pp. 2724-2742. ISSN 0888-5885 (2015) [Refereed Article]
Copyright Statement
Copyright 2015 American Chemical Society
Abstract
Statistical fault detection techniques are able to detect fault and diagnose root-cause(s) from the monitored
process variables. For complex process operations, it is not feasible to screen all the process variables due to monitoring cost and
flooding of alarms. Thus, if a fault is originated from a process variable that is not monitored, conventional statistical techniques
are incapable of locating the true root-cause. To relax this limitation, a two-stage fault diagnosis technique is proposed for process
operations. In the first-stage, the modified independent component analysis is used for fault detection and to identify the faulty
monitored variable. In the second-stage, a Bayesian Network model is constructed considering the process variables and their
dependence obtained from the process flow diagram. Evidence is then generated at the network node corresponding to the faulty
variable identified in the first-stage. Subsequently, the network is updated and analyzed using deductive and abductive reasoning
to identify the true root-cause. To verify the applicability of the proposed technique it is tested on two process models. The
results of both case studies have demonstrated the effectiveness of the proposed technique to diagnose the true root-cause that
originated from process variables that are not monitored. Once integrated with process loss functions, the proposed technique
will serve as an important element of dynamic operational risk management framework.
Item Details
| Item Type: | Refereed Article |
|---|---|
| Keywords: | Modified Independent Component Analysis, Bayesian Network, Fault Diagnosis |
| Research Division: | Engineering |
| Research Group: | Chemical engineering |
| Research Field: | Process control and simulation |
| Objective Division: | Expanding Knowledge |
| Objective Group: | Expanding knowledge |
| Objective Field: | Expanding knowledge in the mathematical sciences |
| UTAS Author: | Yu, H (Mr Hongyang Yu) |
| UTAS Author: | Khan, F (Professor Faisal Khan) |
| UTAS Author: | Garaniya, V (Dr Vikram Garaniya) |
| ID Code: | 100047 |
| Year Published: | 2015 |
| Web of Science® Times Cited: | 41 |
| Deposited By: | NC Maritime Engineering and Hydrodynamics |
| Deposited On: | 2015-04-27 |
| Last Modified: | 2017-11-03 |
| Downloads: | 0 |
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