Open AccessDevelopment of a combined Artificial Neural Network and Principal Component Analysis technique for Engine Health Monitoring
Author(s) -
Maria Grazia De Giorgi,
Luciano Strafella,
Nicola Menga,
Antonio Ficarella
Publication year - 2022
Publication title -
iop conference series. materials science and engineering
Language(s) - English
Resource type - Journals
eISSN - 1757-899X
pISSN - 1757-8981
DOI - 10.1088/1757-899x/1226/1/012035
Subject(s) - artificial neural network , principal component analysis , computer science , aerospace , feedforward neural network , component (thermodynamics) , fuel efficiency , artificial intelligence , engineering , machine learning , data mining , automotive engineering , physics , thermodynamics , aerospace engineering
In aerospace sector, reliability is a crucial point. Modern technologies widely use Artificial Intelligence (AI) algorithms together with detections by sensors in order to design a health-based maintenance plan which stops an aircraft only when needed. In this work, an Engine Health Monitoring (EHM) system was developed by exploiting AI algorithms as Artificial Neural Networks (ANNs) trained to estimate a series of Performance Parameters (PPs) used as index of the health status of the main components constituting an engine. A neural network called Feed-Forward Neural Network (FFNN) in combination with a Principal Component Analysis (PCA) for feature reduction was used in this paper. The software Gas turbine Simulation Program (GSP) was used to generate a series of data containing information about engine performance under different flight conditions and compressor degradation levels. The datasets were subsequently used to train the neural networks to estimate the PPs of the degraded component. The final purpose of the present work is to develop an efficient diagnostic system useful to increase flight safety and decrease maintenance costs and fuel consumption.