A Deep Learning Approach for Fault Diagnosis of Hydrogen Fueled Micro Gas Turbines

Abstract

Hydrogen fueled gas turbines are susceptible to rigorous health degradation in form of corrosion and erosion in the turbine section of a retrofitted gas turbine due to drastically different thermophysical properties of flue gas stemming from hydrogen combustion. In this context fault diagnosis of hydrogen fueled gas turbines becomes indispensable. To authors knowledge, there is a scarcity of fault diagnosis studies for retrofitted gas turbines considering hydrogen as a potential fuel. The present study, however, develops an artificial neural network (ANN) based fault diagnosis model using MATLAB environment. Prior to fault detection, isolation and identification modules, physics-based performance data of 100 kW micro gas turbine (MGT) was synthesized using GasTurb tool. ANN based classification algorithm showed a 99.4% classification accuracy of fault detection and isolation. Moreover, the feedforward neural network-based regression algorithm showed quite good training, testing and validation accuracies in terms of root mean square error (RMSE). The study revealed that presence of hydrogen induced corrosion fault (both as single corrosion fault or as simultaneous fouling and corrosion) led to false alarms thereby prompting other wrong faults during fault detection and isolation modules. Additionally, performance of fault identification module for hydrogen fuel scenario was found to be marginally lower than that of natural gas case due to assuming small magnitudes of faults arising from hydrogen induced corrosion.