We present in this paper a case study of the probabilistic approach to model-based diagnosis. Here, the diagnosed system is a real-world electrical power system (EPS), i.e., the Advanced Diagnostic and Prognostic Testbed (ADAPT) located at the NASA Ames Research Center. Our probabilistic approach is formally well founded and based on Bayesian networks (BNs) and arithmetic circuits (ACs). We pay special attention to meeting two of the main challenges often associated with real-world application of model-based diagnosis technologies: model development and real-time reasoning. To address the challenge of model development, we develop a systematic approach to representing EPSs as BNs, supported by an easy-to-use specification language. To address the real-time reasoning challenge, we compile BNs into ACs. AC evaluation (ACE) supports real-time diagnosis by being predictable, fast, and exact. In experiments with the ADAPT BN, which contains 503 discrete nodes and 579 edges and produces accurate results, the time taken to compute the most probable explanation using ACs has a mean of 0.2625 ms and a standard deviation of 0.2028 ms. In comparative experiments, we found that, while the variable elimination and join tree propagation algorithms also perform very well in the ADAPT setting, ACE was an order of magnitude or more faster.

Reasoning based on belief functions and cause-effect hierarchies are combined to create a methodology for enhanced evidential reasoning. A description of the methodology is given as well as examples of the utilization of a complete system. The method provides reasoning about belief among alternatives, is extensible, and can be easily scaled up to large problems. It is asserted that belief manipulation coupled with information about fault history, events, and symptoms is sufficient to secure good diagnostic results.>

This article describes NetGraph, an object-oriented toolset for risk assessment applications. There are two significant aspects of NetGraph functionality. Firstly, NetGraph is a graphical knowledge acquisition tool for risk assessment, designed for robust structuring and efficient use of domain knowledge. A component library facility in NetGraph helps the domain expert to store and easily retrieve frequently encountered objects in complex systems. Time spent in designing fault trees is significantly reduced by the help of a cut/paste facility that allows the expert to easily define new fault trees by retrieving subtrees (components) of previously defined fault trees and putting them together. A second aspect of NetGraph is fault tree analysis. Fault trees designed in NetGraph can be used by either experts or operators to analyze fault behavior and probability of complex systems. A hypertext interface is also being developed to provide efficient communication of risks to individuals.