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Full-Text Articles in Physical Sciences and Mathematics
Developing Large-Scale Bayesian Networks By Composition: Fault Diagnosis Of Electrical Power Systems In Aircraft And Spacecraft, Ole J. Mengshoel, Scott Poll, Tolga Kurtoglu
Developing Large-Scale Bayesian Networks By Composition: Fault Diagnosis Of Electrical Power Systems In Aircraft And Spacecraft, Ole J. Mengshoel, Scott Poll, Tolga Kurtoglu
Ole J Mengshoel
In this paper, we investigate the use of Bayesian networks to construct large-scale diagnostic systems. In particular, we consider the development of large-scale Bayesian networks by composition. This compositional approach reflects how (often redundant) subsystems are architected to form systems such as electrical power systems. We develop high-level specifiations, Bayesian networks, clique trees, and arithmetic circuits representing 24 different electrical power systems. The largest among these 24 Bayesian networks contains over 1,000 random variables. Another BN represents the real-world electrical power system ADAPT, which is representative of electrical power systems deployed in aerospace vehicles. In addition to demonstrating the scalability …
The Diagnostic Challenge Competition: Probabilistic Techniques For Fault Diagnosis In Electrical Power Systems, Brian W. Ricks, Ole J. Mengshoel
The Diagnostic Challenge Competition: Probabilistic Techniques For Fault Diagnosis In Electrical Power Systems, Brian W. Ricks, Ole J. Mengshoel
Ole J Mengshoel
Methods For Probabilistic Fault Diagnosis: An Electrical Power System Case Study, Brian Ricks, Ole J. Mengshoel
Methods For Probabilistic Fault Diagnosis: An Electrical Power System Case Study, Brian Ricks, Ole J. Mengshoel
Ole J Mengshoel
Health management systems that more accurately and quickly diagnose faults that may occur in different technical systems on-board a vehicle will play a key role in the success of future NASA missions. We discuss in this paper the diagnosis of abrupt continuous (or parametric) faults within the context of probabilistic graphical models, more specifically Bayesian networks that are compiled to arithmetic circuits. This paper extends our previous research, within the same probabilistic setting, on diagnosis of abrupt discrete faults. Our approach and diagnostic algorithm ProDiagnose are domain-independent; however we use an electrical power system testbed called ADAPT as a case …