Model-based robust fault detection and isolation of an industrial gas turbine prototype using soft computing techniques

2012 ◽  
Vol 91 ◽  
pp. 29-47 ◽  
Author(s):  
Hasan Abbasi Nozari ◽  
Mahdi Aliyari Shoorehdeli ◽  
Silvio Simani ◽  
Hamed Dehghan Banadaki
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Soft Computing ◽  
Fault Detection And Isolation ◽  
Model Based ◽  
Robust Fault Detection ◽  
Soft Computing Techniques ◽  
Industrial Gas Turbine

scholarly journals Fault Detection and Isolation Based on Dynamic Observers Applied to Gas Turbine Control Sensors

1998 ◽  
Author(s):  
S. Simani ◽  
P. R. Spina ◽  
S. Beghelli ◽  
R. Bettocchi ◽  
C. Fantuzzi
Keyword(s):  
Fault Detection ◽  
Gas Turbine ◽  
Fault Detection And Isolation ◽  
Sensor Faults ◽  
Sensor Fault ◽  
Actual System ◽  
Analytical Redundancy ◽  
Residual Functions ◽  
Sensor Fault Detection ◽  
Industrial Gas Turbine

In order to prevent machine malfunctions and to determine the machine operating state, it is necessary to use correct measurements from actual system inputs and outputs. This requires the use of techniques for the detection and isolation of sensor faults. In this paper an approach based on analytical redundancy which uses dynamic observers is suggested to solve the sensor fault detection and isolation problem for a single-shaft industrial gas turbine. The proposed technique requires the generation of classical residual functions obtained with different observer configurations. The diagnosis is performed by checking fluctuations of these residuals caused by faults.

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