Abstract
Surveillance and supervision systems have a major role in insuring the safety and availability of industrial equipments and installations. Default detection and diagnosis is highly important to facilitate the planning and implementation of curative and preventive actions. Industrial systems are usually governed by different physical phenomena’s and diverse technological components. Bond graph, being a powerful tool based on energetic and multi-physical analysis can be a well-adapted tool in default detection. The resulting Bond Graph model, allows to apply model based diagnosis methods to detect and eventually isolate defaults. In this paper, energetic systems diagnosis problems are discussed by detailing existing diagnosis methods. The proposed modeling tool is then introduced with illustration of different use cases and applications examples. Diagnosis methods based on Bond Graph model are presented, as well as the extension of these methods with uncertain parameters models. Finally, the studied diagnosis method is applied for default detection and isolation using the study case of asynchrony motor.
Highlights
Any failure of a process is harmful in an environment where performance is paramount
The modeling of the different sections of the pumping system that we will describe its diagnostic in this work was presented and investigated in [1]
In the case where the uncertainty is introduced additively as a BG member (Bond Graph), the structural properties of the uncertain model prevent the automatic generation of the robust RRAS and generate the errors in the simulated model
Summary
Abstract—Surveillance and supervision systems have a major role in insuring the safety and availability of industrial equipments and installations. Default detection and diagnosis is highly important to facilitate the planning and implementation of curative and preventive actions. Industrial systems are usually governed by different physical phenomena’s and diverse technological components. Bond graph, being a powerful tool based on energetic and multi-physical analysis can be a welladapted tool in default detection. The resulting Bond Graph model, allows to apply model based diagnosis methods to detect and eventually isolate defaults. Energetic systems diagnosis problems are discussed by detailing existing diagnosis methods. Diagnosis methods based on Bond Graph model are presented, as well as the extension of these methods with uncertain parameters models. The studied diagnosis method is applied for default detection and isolation using the study case of asynchrony motor
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