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Health monitoring of gas turbine engine using principal component analysis approach

Monitoring health condition of Gas Turbine Engines (GTE), using data-based method requires a large amount of systems' parameter variables (data). These data are nonlinear and of high dimension. To analyse these data, dimensionality reduction techniques are required to transform the high-dimensional data to a lower dimensional space. This study adopts Principal Component Analysis (PCA) technique to analyse the GTE data to extract features from the data which are then compared with feature from the normal operating condition of the GTE for change detection. GTE generates a huge amount from the numerous measured variables. We consider twelve measured variables and seven operating conditions from three-hundred gas turbines. Nearest-neighbour classification of the training data is explored for GTE fault diagnosis. We achieve visualization of the low-dimensional data in two-dimension using scatter plot. M-fold cross-validation is employed to test the performance of our model. The model is implemented in Matlab and C++ programming tool. This model serves as predictive maintenance strategy with efficiency and cost effective. It also helps to minimize the downtime of gas turbine engines, improves safety plant operations. Thus enhances system reliability and availability.