Premium
Fault diagnosis of a simulated industrial gas turbine via identification approach
Author(s) -
Simani S.
Publication year - 2007
Publication title -
international journal of adaptive control and signal processing
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.73
H-Index - 66
eISSN - 1099-1115
pISSN - 0890-6327
DOI - 10.1002/acs.924
Subject(s) - gas turbines , robustness (evolution) , fault detection and isolation , residual , kalman filter , matlab , monte carlo method , computer science , redundancy (engineering) , control engineering , reliability engineering , engineering , algorithm , artificial intelligence , mechanical engineering , biochemistry , chemistry , statistics , mathematics , actuator , gene , operating system
In this paper, a model‐based procedure exploiting the analytical redundancy principle for the detection and isolation of faults on a simulated process is presented. The main point of the work consists of using an identification scheme in connection with dynamic observer and Kalman filter designs for diagnostic purpose. The errors‐in‐variables identification technique and output estimation approach for residual generation are in particular advantageous in terms of solution complexity and performance achievement. The proposed tools are analysed and tested on a single‐shaft industrial gas turbine MATLAB/SIMULINK ® simulator in the presence of disturbances, i.e. measurement errors and modelling mismatch. Selected performance criteria are used together with Monte‐Carlo simulations for robustness and performance evaluation. The suggested technique can constitute the design methodology realising a reliable approach for real application of industrial process FDI. Copyright © 2006 John Wiley & Sons, Ltd.