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A novel testability model for health management of heading attitude system

Chinese Journal of Aeronautics 2013年第1期26卷 201-208页

作者： Liu Guanjun Yang Shuming Qiu Jing Yang Peng Laboratory of Science and Technology on Integrated Logistics Support College of Mechatronics Engineering and Automation National University of Defense Technology

Prognostics and health management （PHM） is very important to guarantee the reliability and safety of aerospace systems, and sensing and test are the precondition of PHM. Integrating design for testability into early design stage of system early design stage is deemed as a fundamental way to improve PHM performance, and testability model is the base of testability analysis and design. This paper discusses a hierarchical model-based approach to testability modeling and analysis for heading attitude system health management. Quantified directed graph, of which the nodes represent components and tests and the directed edges represent fault propagation paths, is used to describe fault-test dependency, and quantitative testability information is assigned to nodes and directed edges. The fault dependencies between nodes can be obtained by functional fault analysis methodology that captures the physical architecture and material flows such as energy, heat, data, and so on. By incorporating physics of failure models into component, the dynamic process of a failing or degrading component can be projected onto system behavior, i.e., system symptoms. Then, the analysis of extended failure modes, mechanisms and effects is utilized to construct fault evolution-test dependency. Using this integrated model, the designers and system analysts can assess the test suite＇s fault detectability, fault isolability and fault predictability. And heading attitude system application results show that the proposed model can support testability analysis and design for PHM very well.

关键词： Fault evolution Functional fault analysis physics of failure model Prognostics and health management Quantified directed graph Testability analysis

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