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Sensor Fault Detection and Identification via Bayesian Belief Networks
Conference proceeding

Sensor Fault Detection and Identification via Bayesian Belief Networks

Nasir Mehranbod, Masoud Soroush, Michael Piovoso, Babatunde Ogunnaike and AAC
2003 American Control Conference; Denver, CO; USA; 4-6 June 2003, v 6, pp 4863-4868
04 Jun 2003
DOI: https://doi.org/10.1109/ACC.2003.1242493
Featured in Collection :   UN Sustainable Development Goals @ Drexel

Abstract

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 parameters (prior and conditional probability data) are 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.

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Web of Science research areas
Automation & Control Systems
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