April 18, 2024

Sobhi Baniardalani

Academic rank: Assistant professor
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Education: Ph.D in Electronic Engineering
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Faculty: Faculty ofٍٍ Electrical Engineering

Research

Title
Qualitative model based fault diagnosis using a threshold level
Type Article
Keywords
α-consistency, Qualitative model, Qualitative model based fault diagnosis, Threshold level, Stochastic automaton.
Researchers Sobhi Baniardalani، Javad Askari، Jane Lunze

Abstract

This paper deals with the effect of using a threshold level in qualitative model based fault diagnosis algorithm. By introducing the concept of α-consistency, a fault diagnosis algorithm is presented in this paper. In this method for each measured input and output signal, a measure of consistency is computed for each fault. If this measure is less than a threshold level α, then the observed input and output are not consistent with the fault. Therefore the fault is excluded from the set of the possible faults. In order to illustrate the proposed method, this algorithm is applied on a 2-tank system. The obtained results show that the faults can be isolated faster. Furthermore to illustrate the diagnosis reliability, a confidence interval is defined. This interval determines the probability of correct fault diagnosis.