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arXiv · 2110.08820

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

Richa Singh
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Abstract

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.

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Richa Singh. 2021-10-17. On-board Fault Diagnosis of a Laboratory Mini SR-30 Gas Turbine Engine. https://arxiv.org/abs/2110.08820

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