Fault classification has become one of the main features in gas turbine health monitoring. Hence techniques such as gas path analysis, artificial neural networks, expert systems, fuzzy logic and many others have been developed for this purpose in the past. In this paper, an alternative rough set based diagnostic method using enhanced fault signatures combined with three fault classification frameworks for gas turbine fault classification have been introduced, i.e. Framework 1 with a single step to classify single and dual component faults, Framework 2 with the first step to identify weather it is a single or dual component faults and the second step to identify the individual faults, and Framework 3 with the first step to identify faults associated with component types and the second step to identify the individual faults. Such frameworks have been applied to the fault classification of a model two-spool turbofan gas turbine engine implemented with single and dual component faults to test the effectiveness of the frameworks. It has been demonstrated in the application that all three framework configurations can provide satisfactory fault classification and that Framework 1 has higher fault classification success rate than that of Frameworks 2 and 3. In addition, Frameworks 2 and 3 have better performance in identifying fault types than Framework 1.

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