Application of Model-based and Data-driven Techniques in Fault Diagnosis

Model-based fault diagnosis, using statistical techniques, residual generation (by analytical redundancy), and parameter estimation, has been an active area of research for the past four decades. However, these techniques are developed in isolation and generally a single technique can not address the diagnostic problems in complex systems. In this paper, we investigate a hybrid approach, which combines different techniques to obtain better diagnostic performance than the use of a single technique alone, and demonstrate it on a fault detection and diagnosis (FDD) system.