This paper presents a new automated diagnosis methodology which attempts to bridge the gap between reliability at the design phase and diagnosis at the usage phase. The methodology takes advantage of dynamic fault tree qualitative and quantitative data to develop a diagnostic importance measure. The methodology produces a diagnostic decision tree based on the fault tree and on the diagnostic importance measure. To enhance the diagnosis process the presented methodology incorporates evidence from sensors to improve diagnosis.
Automatic Diagnosis via Sensors Modeled by Dynamic Fault Trees
Sae Technical Papers
SAE 2005 World Congress & Exhibition ; 2005
2005-04-11
Conference paper
English
Automatic diagnosis via sensors modeled by dynamic fault trees
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