Combining Machine Learning, Embedded Sensor Networks and Additive Burner Design for Combustor Structural Health Monitoring

Abstract

Abstract The crystAIr project is about sensitising burners for flame monitoring based on a discrete number of dynamic pressure sensors and a machine learning approach. The context is the development of better aircraft engines and the prospect of technical solutions for hydrogen-fuelled gas turbines. The combination of sensors and appropriate signal processing is designed to mimic a ‘feel’ for the state of the flame. The idea is to monitor the gas turbine's correct operation in real-time and anticipate impending problems such as flame blowout, combustion instability or flashback. Compared to conventional flame monitoring based on signal sampling, thresholding, band-pass analysis and time-lag measurement, this method should be less computationally intensive, more sensitive and more robust to artefacts. Not only should it be more responsive, but it should also anticipate problems based on a lessons-learned approach and outperform the conventional precursors. An unassisted machine learning method is used to achieve this. A custom-built AM burner designed for this experiment provides multiple instrumentation options. A powerful data acquisition system is set up to collect data on multiple channels, over a long time duration and at high speed to collect the learning signal chunks. The focus is on the detection of flames and, more specifically, the moment of their ignition and extinction, the operating conditions that are prone to flashback and the stability of the combustion. Additional measurement techniques are used to refine the learning method. The paper covers all these points and presents the first results.