Build better diagnostic decision trees

Enhancing the ability to perform diagnostics on a system that has failed can significantly impact maintenance and repair costs. A good diagnostic tool enables a user to analyze a failed system and identify the failed components. While the field of diagnostics is not a modern one, the way in which system diagnostics are performed is continuously changing. The automatic diagnosis based on reliability analysis (ADORA) methodology utilizes reliability information developed during the design phase to build a diagnostic map. Previous work on ADORA demonstrated how a diagnostics procedure can be performed on a system that has been analyzed using a static reliability model, particularly a fault tree (Assaf and Dugan, 2003). In this article, we extend the ADORA methodology to utilize reliability analysis of dynamic fault trees (DFTs), which are reliability models that capture sequences and combinations of component failures that induce system failure. DFTs are particularly well suited for analyzing computer-based systems.As an example the common rail fuel injection system is discussed.