Diagnosis of continuous dynamic systems: integrating consistency-based diagnosis with machine-learning techniques

This paper describes an integrated approach to diagnosis of complex dynamic systems, combining model based diagnosis with machine learning techniques, proposing a simple framework to make them cooperate, hence improving the diagnosis capabilities of each individual method.First step in the diagnosis process resorts to consistency-based diagnosis, via possible conflicts,which allows fault detection and localization without prior knowledge of the device fault modes. In the second step, a classification system, obtained via machine learning techniques, is used topropose a ranked sequence of fault modes, coherent with the previous localization step. This cycle iterates in time, generating more focused and precise diagnosis as new data are available.A laboratory plant has been built to test this proposal. Simulation results are shown for a totalnumber of 14 different faults.