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.
[1]
K. Kroschel,et al.
Applying Bayesian networks to fault diagnosis
,
1994,
1994 Proceedings of IEEE International Conference on Control and Applications.
[2]
Coleman B. Brosilow,et al.
Nonlinear model predictive control of styrene polymerization at unstable operating points
,
1990
.
[3]
Ann E. Nicholson,et al.
Sensor Validation Using Dynamic Belief Networks
,
1992,
UAI.
[4]
Nugroho Iwan Santoso,et al.
Nuclear plant fault diagnosis using probabilistic reasoning
,
1999,
1999 IEEE Power Engineering Society Summer Meeting. Conference Proceedings (Cat. No.99CH36364).
[5]
Mark A. Kramer,et al.
Comparison of belief networks and rule-based expert systems for fault diagnosis of chemical processes
,
1993
.