Sensor fault detection and identification via Bayesian belief networks

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.