A Bayesian network dealing with measurements and residuals for system monitoring

The purpose of this paper is to present an original method for system monitoring with Bayesian networks. Our proposal is to associate a data-driven method to another model-based under a common tool. The two methods are first modeled under a Bayesian network (conditional Gaussian network), and then combined to evaluate the system state. In the proposed framework the residuals and measures coexist under a probabilistic framework. This approach is tested on a simulation of a water heater process under some various circumstances and shows better results than the two methods used alone.