In this paper 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.
Application of a neural network in gas turbine control sensor fault detection / Simani, S.; Fantuzzi, C.; Spina, P. R.. - 1:(1998), pp. 182-186. (Intervento presentato al convegno Proceedings of the 1998 IEEE International Conference on Control Applocations. Part 1 (of 2) tenutosi a Trieste, Italy, nel 1998).
Application of a neural network in gas turbine control sensor fault detection
Simani S.;Fantuzzi C.;Spina P. R.
1998
Abstract
In this paper 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.
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