GA based fault parameter identification for hybrid system with unknown mode changes

Ming Yu; Danwei Wang; Shai A. Arogeti

doi:10.1109/ISSCAA.2008.4776133

GA based fault parameter identification for hybrid system with unknown mode changes

Ming Yu, Danwei Wang, Shai A. Arogeti

Research output: Contribution to conference › Paper › peer-review

10 Scopus citations

Abstract

This paper presents a method for the identification of the fault parameters of hybrid systems with unknown mode changes after fault occurring. The identification method utilizes Genetic Algorithm (GA) to identify fault parameters and unknown mode changes simultaneously based on Global Analytical Redundancy Relation (GARR). Fault parameters and mode change time of all switches are encoded into one chromosome as potential solution of the identification process. The GARR is adopted as the performance index of GA search. With the fault parameter values identified by the proposed method, we can tell the healthy status of monitored hybrid system. Experiment results show the efficiency of the proposed method.

Original language	English
DOIs	https://doi.org/10.1109/ISSCAA.2008.4776133
State	Published - 1 Dec 2008
Externally published	Yes
Event	2008 2nd International Symposium on Systems and Control in Aerospace and Astronautics, ISSCAA 2008 - Shenzhen, China Duration: 10 Dec 2008 → 12 Dec 2008

Conference

Conference	2008 2nd International Symposium on Systems and Control in Aerospace and Astronautics, ISSCAA 2008
Country/Territory	China
City	Shenzhen
Period	10/12/08 → 12/12/08

ASJC Scopus subject areas

Aerospace Engineering
Control and Systems Engineering

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10.1109/ISSCAA.2008.4776133

Cite this

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title = "GA based fault parameter identification for hybrid system with unknown mode changes",

abstract = "This paper presents a method for the identification of the fault parameters of hybrid systems with unknown mode changes after fault occurring. The identification method utilizes Genetic Algorithm (GA) to identify fault parameters and unknown mode changes simultaneously based on Global Analytical Redundancy Relation (GARR). Fault parameters and mode change time of all switches are encoded into one chromosome as potential solution of the identification process. The GARR is adopted as the performance index of GA search. With the fault parameter values identified by the proposed method, we can tell the healthy status of monitored hybrid system. Experiment results show the efficiency of the proposed method.",

author = "Ming Yu and Danwei Wang and Arogeti, {Shai A.}",

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AU - Yu, Ming

AU - Wang, Danwei

AU - Arogeti, Shai A.

PY - 2008/12/1

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N2 - This paper presents a method for the identification of the fault parameters of hybrid systems with unknown mode changes after fault occurring. The identification method utilizes Genetic Algorithm (GA) to identify fault parameters and unknown mode changes simultaneously based on Global Analytical Redundancy Relation (GARR). Fault parameters and mode change time of all switches are encoded into one chromosome as potential solution of the identification process. The GARR is adopted as the performance index of GA search. With the fault parameter values identified by the proposed method, we can tell the healthy status of monitored hybrid system. Experiment results show the efficiency of the proposed method.

AB - This paper presents a method for the identification of the fault parameters of hybrid systems with unknown mode changes after fault occurring. The identification method utilizes Genetic Algorithm (GA) to identify fault parameters and unknown mode changes simultaneously based on Global Analytical Redundancy Relation (GARR). Fault parameters and mode change time of all switches are encoded into one chromosome as potential solution of the identification process. The GARR is adopted as the performance index of GA search. With the fault parameter values identified by the proposed method, we can tell the healthy status of monitored hybrid system. Experiment results show the efficiency of the proposed method.

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Y2 - 10 December 2008 through 12 December 2008

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GA based fault parameter identification for hybrid system with unknown mode changes

Abstract

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ASJC Scopus subject areas

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