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To avoid disrepair and over-repair and improve gas turbine reliability and availability, gas-path diagnosis is an effective technical means of disseminating early warning information for evolving or impending deterioration. Aiming at the problems of existing gas-path diagnosis methods (i.e., data-driven based gas-path diagnosis and a model-based gas-path diagnosis), this paper proposes a novel gas-path diagnosis method based on model-data hybrid drive, which is a forward solving mathematical process, to ensure real-time monitoring performance. Through case analysis, the proposed diagnostic method is not limited by the intrinsic nonlinear shape change of the characteristic maps of the actual component, which has good diagnostic applicability. And after the quadratic feature extraction of the two-dimensional entropy features (i.e., Shannon entropy and exponential entropy features), it is convenient to obtain visualized gas turbine gas-path diagnosis results for the operation and maintenance personnel. Moreover, although the extracted two-dimensional entropy values will change slightly when the operating conditions change, it can maintain good inter-class separation and intra-class aggregation performance.

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