Affiliation: | aDipartimento di Ingegneria, Università di Ferrara, Via Saragat 1, 44100 Ferrara, Italy bDepartment of Engineering, University of Hull, Cottingham Road, Hull, HU6 7RX, United Kingdom |
Abstract: | In this work, a model-based procedure exploiting analytical redundancy for the detection and isolation of faults on a gas turbine simulated process is presented. The main point of the paper consists of exploiting an identification scheme in connection with dynamic observer or filter design procedures for diagnostic purposes. Thus, black-box modelling and output estimation approaches to fault diagnosis are in particular advantageous in terms of solution complexity and performance achieved. Moreover, the suggested scheme is especially useful when robust solutions are considered for minimising the effects of modelling errors and noise, while maximising fault sensitivity. In order to experimentally verify the robustness of the solution obtained, the proposed FDI strategy has been applied to the simulation data of a single-shaft industrial gas turbine plant in the presence of measurement and modelling errors. Hence, extensive simulations of the test-bed process and Monte Carlo analysis are the tools for assessing experimentally the capabilities of the developed FDI scheme, when compared also with different data-driven diagnosis methods. |