Paper Details: Downloads: 541
Serial Number: P1110708003
Title: State and Fault Parameter Estimation Applied To Three-Tank Bench Mark Relying On Augmented State Kalman Filter
Authors: S.Abraham Lincon and D.Sivakumar and J.Prakash
Abstract: Fault detection and diagnosis (FDD) can be described as early determination (detection) and localization (diagnosis) of faulty elements in a dynamic system. In this paper, a model based approach to detect and diagnose abrupt and slowly varying faults in a three-tank benchmark system is developed. The Fault detection and diagnosis scheme is formulated as a state estimation problem by considering the fault parameter as an additional state. It is then solved as a simultaneous state and fault parameter estimation using Augmented State Kalman Filter (ASKF) and Two Stage Kalman Filter (TSKF). Extensive simulation studies performed on three tank bench mark system reveal that the FDD scheme is capable of generating reasonably accurate state and fault parameter estimates in the presence of process and measurement noises. This holds good for different types of faults. Further the performance of ASKF is compared with that of TSKF.
Keywords: Augmented State Kalman Filter(ASKF),Two Stage Kalman Filter (TSKF),Fault Detection and Diagnosis (FDD),Three-Tank System
Journal/Conference: International Journal of Automatic Control and System Engineering
Volume: 7
Issue: 1
Submission Date: 2/1/2007 12:00:00 AM
Review Date: 3/1/2007 12:00:00 AM
Publishing Date: 5/1/2007 12:00:00 AM
Article Downloads: 541
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