Feed-Forward Neural Network for health monitoring of a parallel hybrid electric power system

Abstract

Hybrid engines are becoming more and more widespread. Electric energy instead is a valid help to reduce the environmental impact. In hybrid engines, the number of components is higher and this results in a decrease in reliability. With Engine Health Monitoring (EHM) we mean the set of techniques used to monitor the health status of a system based on the values assumed by some related parameters. Artificial Intelligence (AI) methods are widely used nowadays in this discipline. In this paper, an EHM approach was developed to monitor the health status of some components constituting an hybrid turboshaft. The dynamic model of the hybrid electric power system is described in an accompanying paper. Feed-Forward Neural Network (FFNN) is used as AI tool to built the just cited system. The engine modelled with Simulink, was used to perform a series of steady-state simulations implementing a degradation condition in some selected components. The degradation condition was simulated by changing the value of the Performance Parameters (PPs) related to each of the selected components. The results of the simulation were used to obtain a dataset useful to train the FFNN to predict the values of the same PPs in a degraded case.