Detection and Isolation of Sensor and Process Faults in a Heat Exchanger Using a Fuzzy-Model Library

Abstract

In this contribution, an approach to model-based fault detection and isolation (FDI) of sensor and process faults for nonlinear processes is presented. The process is decomposed into several subprocesses and for each a nonlinear model is identified. This model library consists of Takagi-Sugeno type fuzzy models and is used to generate several estimates for process outputs and states. Comparing these estimates with measured signals leads to residuals which indicate the state of the system and provide information about the source of possible faults. Implementation of the models either as a parallel model or as a series-parallel model lead to different FDI results. In this contribution this different sensitivity is investigated also. The applicability of the multi-model based FDI is illustrated on an industrial scale thermal plant. Seven different process faults and eight different sensor faults can be detected.