DIAGNOSIS OF CONTINUOUS DYNAMIC SYSTEMS: INTEGRATING CONSISTENCY BASED DIAGNOSIS WITH MACHINE-LEARNING TECHNIQUES

摘要

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 to propose 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 total number of 14 different faults.