Abstract
An efficient maintenance plan is an important aspect for aeronautical companies to increase flight safety and decrease costs. Modern technologies are widely used in the Engine Health Monitoring (EHM) discipline to develop intelligent tools capable of monitoring the health status of engines. In this work, Artificial Neural Networks (ANNs) and in-detail Feed-Forward Neural Networks (FFNNs) were exploited in addition to a Kernel Principal Component Analysis (KPCA) to design an intelligent diagnostic tool capable of predicting the Performance Parameters (PPs) of the main components used as their health index. For this purpose, appropriate datasets containing information about degraded engines were generated using the Gas Turbine Simulation Program (GSP). Finally, the original datasets and the reduced datasets obtained after the application of KPCA to the original datasets were both used in the training and testing process of neural networks, and results were compared. The goal was to obtain a reliable intelligent tool useful for diagnostic purposes. The study showed that the degraded component detection and estimation of its performance achieved by using the hybrid KPCA–FFNNs were predicted with accurate and reliable performance, as demonstrated through detailed quantitative confusion matrix analysis.
Highlights
Diagnostics is nowadays a key concept used to ensure flight safety and cost reduction
In [14], MultiGene Genetic Programming (MGGP) is used to obtain a relationship between Exhaust Gas Temperature (EGT) and other variables characterizing an engine, and in the same work, Nonlinear AutoRegressive with eXogenous inputs neural network are exploited for a one-step-ahead prediction tool
Results show that the training of an Associative Neural Networks (AANNs) model takes more time than it takes for a linear Principal Component Analysis (PCA), the former is more suitable to be applied in nonlinear systems
Summary
Diagnostics is nowadays a key concept used to ensure flight safety and cost reduction. Erosion has the same effect of fouling on the compressor, while in the case of turbine erosion, efficiency continues to decrease but flow capacity increases [5]. Fan blade erosion due to volcanic ash is investigated using computational fluid dynamics tools considering two different approaches, one in which the erosion rate is considered constant and the other in which it was updated over time In both cases, the results show an increase in material removed when moving from the midspan toward the tip. Several studies have used Principal Component Analysis (PCA) This technique has a reduced performance when applied to strongly non-linear problems, such as in the case of health monitoring of an aircraft engine. Degradation was simulated by a decrease in the efficiencies that characterize them
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