On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine

Inspired by recent progress in machine learning, a data-driven fault diagnosis and isolation (FDI) scheme is explicitly developed for failure in the fuel supply system and sensor measurements of the laboratory gas turbine system. A passive approach to fault diagnosis is implemented where a model is trained using machine learning classifiers to detect a given set of faults in real-time on which it is trained. Towards the end, a comparative study is presented using well-known classification techniques, namely Support Vector Machine, Linear Discriminant Analysis, K-Neighbors, and Decision Trees. Several simulation studies were carried out to demonstrate the proposed fault diagnosis scheme's advantages, capabilities, and performance.