An intelligent data filtering and fault detection method for gas turbine engines

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Abstract
In a gas turbine fault diagnostics, the removal of measurement noise and data outliers prior to the fault analysis is very essential. The conventional filtering methods, particularly the linear ones, are not sufficiently accurate, which might possibly lead to the loss of critically important features in the fault analysis process. Conversely, the recorded accuracies obtained from the non-linear filters are promising. Recently, the focus has been shifted to the artificial neural network (ANN) based nonlinear filters due to their capability of providing a robust identity map between the input and output data, which can be efficiently exploited in the process of fault diagnosis. This paper aims to present combined auto-associative neural network (AANN) and K-nearest neighbor (KNN) based noise reduction and fault detection method for a gas turbine engine application. The performance of the developed method has been evaluated using data obtained from a model simulation. The test results revealed that the developed hybrid method is more effective and reliable than the conventional methods for the fault detection of the gas turbine engine with negligible false alarms and missed detections.

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