Abstract
In industrial processes, to confide the success of planed operation, implementing early and accurate method for recognizing abnormal operating conditions, known as faults, is essential. Effective method for fault detection and diagnosis helps reducing impact of these faults, extols the safety of operation, minimizes down time and reduces manufacturing costs. In this paper, application of BBNs is studied for a benchmark chemical industrial process, known as, Tennessee Eastman in order to achieve early fault detection and accurate probable diagnosis of their causes. Application of Bayesian belief networks for fault detection and diagnosis of Tennessee Eastman process in the graphical context description has not been tested yet. Success of this feature confirms capability and ease use of it as a diagnostic system in actual industrial processes.
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