Coupling data-driven and model-based methods to improve fault diagnosis

Abstract

Monitoring a system is often not an easy task and the best approach to address it would be to develop a monitoring system that uses data, expert knowledge, and mathematical models. Combining these three sources of information on the system is often unpractical of various reasons such as in complex systems. In this paper, a hybrid method for diagnosing single and multiple simultaneous faults, while considering unknown operating conditions, is proposed. This method consists of a Bayesian classifier combining statistical decisions and fault signature matrix. Several scenarios of operating conditions are simulated for illustrative purposes. The results, in terms of classification rates, show the interest of the hybrid classifier. It demostrates higher capabilities to isolating multiple simultaneous faults that the purely data-driven classifier fails to accomplish.