In the context of process safety, significant improvements are needed in fault detection methods, especially, in the areas of early detection and warning. In this article, a multivariate risk-based fault detection and diagnosis technique is proposed. The key elements of this technique are to eliminate faults that are not serious and to provide a dynamic process risk indication at each sampling instant. A multivariable residual generation process based on the Kalman filter has been combined with a risk assessment procedure. The use of the Kalman filter makes the method more robust to false alarms, which is an important aspect of any fault detection algorithm that targets the safety of a process. In addition, we consider significant differences in the severity of the faults associated with different process variables. We also take into account the varying intensity of damage caused by the increasing and decreasing rates of fault and the need to treat those cases differently.

Item Type:	Refereed Article
Keywords:	dynamic risk assessment, Kalman filter, multivariate fault diagnosis, risk assessment of process system, assessment of process, assessment procedure, dynamic risk assessments, fault detection algorithm, fault detection and diagnosis, Kalman filters
Research Division:	Engineering
Research Group:	Engineering practice and education
Research Field:	Risk engineering
Objective Division:	Expanding Knowledge
Objective Group:	Expanding knowledge
Objective Field:	Expanding knowledge in engineering
UTAS Author:	Khan, FI (Professor Faisal Khan)
ID Code:	120763
Year Published:	2013
Web of Science® Times Cited:	28
Deposited By:	NC Maritime Engineering and Hydrodynamics
Deposited On:	2017-08-30
Last Modified:	2017-11-06
Downloads:	0