On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine
This work addresses fault detection for gas turbine systems, but it is incremental as it applies existing classification methods to a specific laboratory setup.
The paper tackled fault diagnosis in a gas turbine engine's fuel supply and sensors by developing a data-driven scheme using machine learning classifiers, and it demonstrated the approach's performance through simulation studies.
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 of fault diagnosis is implemented where a model is trained using machine learning classifiers to detect a given set of fault scenarios in real-time on which it is trained. Towards the end, a comparative study is presented for well-known classification techniques, namely Support vector classifier, linear discriminant analysis, K-neighbor, and decision trees. Several simulation studies were carried out to demonstrate and illustrate the proposed fault diagnosis scheme's advantages, capabilities, and performance.