Abstract
This paper presents a novel approach to solving the probabilistic diagnosis problem in multiprocessor systems. The main idea of the algorithm is based on the reformulation of the error propagation model as a Process Network Synthesis (PNS) problem. This idea is illustrated by deriving a maximum likelihood decision procedure. The diagnostic accuracy of the solution is considered on the basis of simulation measurements, and a method of constructing a general framework for different aspects of a complex problem with the use of P-Graph models is demonstrated.
This research has been supported in part by the Hungarian National Research Foundation Grants OTKA T038027.
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Polgár, B., Selényi, E., Bartha, T. (2002). Modeling Uncertainty in System-Level Fault Diagnosis Using Process Graphs. In: Kacsuk, P., Kranzlmüller, D., Németh, Z., Volkert, J. (eds) Distributed and Parallel Systems. The Springer International Series in Engineering and Computer Science, vol 706. Springer, Boston, MA. https://doi.org/10.1007/978-1-4615-1167-0_23
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DOI: https://doi.org/10.1007/978-1-4615-1167-0_23
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