Jet Engine Health Monitoring using Hyperspectral Imaging and Artificial Intelligence

In the project we propose to develop a method that combines non-intrusive hyperspectral imaging (HSI) and analytical-based Artificial Intelligence (AI) models to assess the engine health via remote measurement of the engine exhaust gases. Although the specific characteristics of exhaust gas emissions, or "spectral signature" of the jets have already proven successful in the monitoring of the engine performance and operation quality, we propose using AI and Deep Learning (DL) to combine such knowledge with engine data from the CETADS to find patterns that would help improve maintenance procedures and explain specific alerts.

The project DAP/23-07 - JEMHy is financed under the DFR call.