Please use this identifier to cite or link to this item: http://scholarbank.nus.edu.sg/handle/10635/22827
Title: Fault detection and isolation with estimated frequency response
Authors: LU JINGFANG
Keywords: fault, diagnosis, residual, statistical, frequency, optimal
Issue Date: 29-Dec-2009
Source: LU JINGFANG (2009-12-29). Fault detection and isolation with estimated frequency response. ScholarBank@NUS Repository.
Abstract: Parameter &faults; of a system are generally addressed via parameter estimation methods. &fault; detection and isolation(FDI) are achieved on the basis of errors in the estimated parameters. FDI with estimated &frequency; response (EFR) is an attractive alternative in that it assumes very little knowledge about the monitored system. In detection, it only assumes that the system is LTI and requires no a priori determination of the order of the plant as long as the number of &frequency; points used in the &frequency; response estimation is much larger than the number of parameters in the system. Another advantage compared to the parameter estimation method is that FDI with EFR lends itself to &statistical; analysis which allows the user to set the false alarm rate in the detection. In this thesis, FDI with EFR is studied. A &fault; is defined to be a change in the plant parameter vector which subsequently alters the &frequency; response of the plant. &fault; detection refers to the identification of when a change in the &frequency; response has occurred while &fault; isolation refers to the identification of the plant parameter in which a change has occurred. Both these are achieved by the construction of a &residual; vector based on the estimated &frequency; response without the specific identification of the parameter vector. The conditions of detectability and isolability (DI) in terms of the &residual; formed from the &frequency; response are first proposed. It was found that all &faults; are detectable if and only if the nominal system is identifiable and the &faults; are isolable when every &fault; is also detectable. Several examples of &residual;s are proposed. Some only satisfy the detectability conditions while others satisfy both detectability and isolability conditions. When using the &residual; formed from EFR, it is assumed that the mean value of the &residual; satisfy conditions of DI. According to these conditions, &residual;s are designed and algorithms for detection and isolation are developed based on hypothesis testing. The performance of the &residual; vector in terms of detection and isolation rates is also studied. In detection, it was found that the detection rate can be improved if the &frequency; response of the system in &fault;y state is known. In isolation, a method to calculate the isolation rate for a given &residual; is developed first. Then the calculated isolation rate is used as a criterion to design an improved &residual;. The performance was verified by simulation.
URI: http://scholarbank.nus.edu.sg/handle/10635/22827
Appears in Collections:Ph.D Theses (Open)

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