An application of a procedure using a neural network for the detection and isolation of faults modeled by step functions in input-output control sensors of a single shaft industrial gas turbine is presented. The real process is modeled as a linear dynamic system corrupted by stochastic additive noise. The diagnosis system involves dynamic observers and utilizes the neural network in order to classify observer residuals into fault classes.

Kalman filtering to enhance the gas turbine control sensor fault detection

SIMANI, Silvio;SPINA, Pier Ruggero
1998

Abstract

An application of a procedure using a neural network for the detection and isolation of faults modeled by step functions in input-output control sensors of a single shaft industrial gas turbine is presented. The real process is modeled as a linear dynamic system corrupted by stochastic additive noise. The diagnosis system involves dynamic observers and utilizes the neural network in order to classify observer residuals into fault classes.
1998
fault diagnosis; gas turbines; kalman filters; observers; fault identification; sensors
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Utilizza questo identificativo per citare o creare un link a questo documento: https://hdl.handle.net/11392/1195645
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