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Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study

Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study,Brian W. Ricks,Ole J. Mengshoel

Methods for Probabilistic Fault Diagnosis: An Electrical Power System Case Study   (Citations: 3)
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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 study. In one set of ADAPT experiments, performed as part of the 2009 Diagnostic Challenge, our system turned out to have the best performance among all competitors. In a second set of experiments, we show how we have recently further significantly improved the performance of the probabilistic model of ADAPT. While these experiments are obtained for an electrical power system testbed, we believe they can easily be transitioned to real-world systems, thus promising to increase the success of future NASA missions.
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    • ...We note that this approach has been very successful for electrical power system diagnosis [5]–[7]...
    • ...[11]. During on-line computation, the arithmetic circuit-based diagnostic software reads commands and sensor data, performs preprocessing including discretization, inputs evidence into the arithmetic circuit, evaluates it, and outputs diagnostic results based on the posterior distribution over the health random variables [5]–[7]...
    • ...As an example, we mention the very successful use of arithmetic circuits, compiled from BNs, for fault diagnosis in electrical power systems that are representative of those found in aerospace vehicles [5], [6]...
    • ...5 Considering BNs for SSHM more specifically, more targeted tools and techniques also exist [5]–[7], [9]...
    • ...The present paper, which builds on previous hardwareoriented research on system health management using BNs [5], [7], [36], is in several ways different from previous work...
    • ...Fifth, we do not use a dynamic BN [25], [28], just a simpler static BN with temporal evidence (see also [5], [7], [36])...

    Johann Schumannet al. Integrated Software and Sensor Health Management for Small Spacecraft

    • ...These modelling approaches and algorithms are again supported by diagnostic software such as Livingstone [1], Hyde [2], or ProDiagnose [3,4]...

    Johann Schumannet al. Who Guards the Guardians? - Toward V&V of Health Management Software -...

    • ...ADAPT. [2] used large-scale Bayesian networks to inference the faults in the Testbed, in their work, a large Bayesian networks could be constructed automaticly through simple description of high-level modeling language, and in the on line diagnosis, the Arithmetic Circuits are applied to perform probabilistic inference...

    Zhang Senet al. Power System Fault Estimation under Non-white Noise

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