  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.
