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Sensor fault detection and identification via Bayesian belief networks

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4 Author(s)
Mehranbod, N. ; Dept. of Chem. Eng., Drexel Univ., Philadelphia, PA, USA ; Soroush, M. ; Piovoso, Michael ; Ogunnaike, B.A.

A new Bayesian belief network (BBN) model with discretized nodes is proposed for fault detection and identification in a single sensor. The single-sensor model is used as a building block to develop a BBN model for all sensors in the process under consideration. A new fault detection index, a fault identification index, and a threshold setting procedure for the multi-sensor model are introduced. Single-sensor model design parameter (prior and conditional probability data) is optimized to achieve maximum effectiveness in detection and identification of sensor faults. The single-sensor model and the optimal values of the design parameters are used to develop a multi-sensor BBN model for a polymerization reactor at steady-state conditions. The capabilities of this BBN model to detect and identify bias, drift and noise in sensor readings are illustrated by an example of simultaneous multiple faults.

Published in:

American Control Conference, 2003. Proceedings of the 2003  (Volume:6 )

Date of Conference:

4-6 June 2003

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