Probabilistic Model-Based Diagnosis: An Electrical Power System Case Study

Abstract--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 pro...

• Type: Journal
• Year: 2010
• Where: TSMC
• Authors: Ole J. Mengshoel, Mark Chavira, Keith Cascio, Scott Poll, Adnan Darwiche, N. Serdar Uckun
• Keywords: Artificial Intelligence, Model-based Diagnosis, Probabilistic Approach, Real-time Reasoning, TSMC 2010