期刊名称：International Journal of Advanced Computer Science and Applications(IJACSA)
印刷版ISSN：2158-107X
电子版ISSN：2156-5570
出版年度：2019
卷号：10
期号：10
DOI：10.14569/IJACSA.2019.0101052
出版社：Science and Information Society (SAI)
摘要：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.
关键词：Bond graph; diagnostic; fault detection; energy systems
