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A Structural Model Decomposition Framework for Hybrid Systems DiagnosisNowadays, a large number of practical systems in aerospace and industrial environments are best represented as hybrid systems that consist of discrete modes of behavior, each defined by a set of continuous dynamics. These hybrid dynamics make the on-line fault diagnosis task very challenging. In this work, we present a new modeling and diagnosis framework for hybrid systems. Models are composed from sets of user-defined components using a compositional modeling approach. Submodels for residual generation are then generated for a given mode, and reconfigured efficiently when the mode changes. Efficient reconfiguration is established by exploiting causality information within the hybrid system models. The submodels can then be used for fault diagnosis based on residual generation and analysis. We demonstrate the efficient causality reassignment, submodel reconfiguration, and residual generation for fault diagnosis using an electrical circuit case study.
Document ID
20150018046
Acquisition Source
Ames Research Center
Document Type
Conference Paper
Authors
Daigle, Matthew
(NASA Ames Research Center Moffett Field, CA United States)
Bregon, Anibal
(Valladolid Univ. Spain)
Roychoudhury, Indranil
(Stinger Ghaffarian Technologies, Inc. (SGT, Inc.) Moffett Field, CA, United States)
Date Acquired
September 17, 2015
Publication Date
August 31, 2015
Subject Category
Systems Analysis And Operations Research
Computer Programming And Software
Report/Patent Number
ARC-E-DAA-TN23822
Meeting Information
Meeting: International Workshop on Principles of Diagnosis
Location: Paris
Country: France
Start Date: August 31, 2015
End Date: September 3, 2015
Sponsors: Centre National de la Recherche Scientifique
Funding Number(s)
CONTRACT_GRANT: NNA14AA60C
WBS: WBS 999182.02.60.01.01
CONTRACT_GRANT: DPI2013-45414-R
Distribution Limits
Public
Copyright
Public Use Permitted.
Keywords
structural model decomposition
diagnosis
hybrid systems
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