Hybrid model identification for fault diagnosis of non-linear dynamic processes

This work addresses an approach for fault diagnosis of industrial processes using hybrid models. A non-linear dynamic process can, in fact, be described as a composition of different affine submodels selected according to the process operating conditions. This paper concerns the identification of the hybrid model parameters through the input-output data acquired from the non-linear process. Therefore, the fault detection scheme adopted to generate residual signals exploits this estimated hybrid model. In order to show the effectiveness of the developed technique, the results obtained in the fault diagnosis of a real industrial plant are finally reported.