Fault identification and analysis Using AI TECHNIQUES for three tank System and deaerator

Abstract

A fault can be defined as an unexpected change or malfunction in a newlineSystem although it may not lead to any physical failure or breakdown A newlinesupervision system should be used to detect and identify the fault and its newlineseverity Research work in the area of fault diagnosis is on the rise due to newlineincreasing demand for reliability operational safety and environmental newlineprotection particularly for safety critical systems such as chemical processes newlinenuclear plants and aircraft If the severity of the fault increases then the newlineconsequences can be extremely serious in terms of human life environmental newlineimpact and or economic loss newlineA fault diagnosis system for on line application must provide newlineguaranteed response with its severity so that a quick decision can be taken newlineeither for periodic maintenance if the severity of the fault is less or an newlineimmediate shut down if the severity is more In this thesis a model based newlinefault diagnosis in a deaerator system has been attempted using Artificial newlineIntelligence AI techniques The presented framework is structurally divided newlineinto fault detection and fault diagnosis that combine qualitative and newlinequantitative detection of fault The significance of the proposed method is the newlineability of diagnosis system which indicates the occurrence of single and newlinedouble fault along with severity newline newline