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The digital twin-driven performance model provides an attractive option for the warn gas-path faults of the gas turbines. However, three technical difficulties need to be solved: (1) low modeling precision caused by individual differences between gas turbines, (2) poor solution efficiency due to excessive iterations, and (3) the false alarm and missing alarm brought by the traditional fixed threshold method. This paper proposes a digital twin model-based early warning method for gas-path faults that breaks through the above obstacles from three aspects. Firstly, a novel performance modeling strategy is proposed to make the simulation effect close to the actual gas turbine by fusing the mechanism model and measurement data. Secondly, the idea of controlling the relative accuracy of model parameters is developed. The introduction of an error module to the existing model can greatly shorten the modeling cycle. The third solution focuses on the early warning based on the digital twin model, which self-learns the alarm threshold of the warning feature of gas-path parameters using the kernel density estimation. The proposed method is utilized to analyze actual measured data of LM2500 +, and the results verify that the new-built digital model has higher accuracy and better efficiency. The comparisons show that the proposed method shows evident superiority in early warning of performance faults for gas turbines over other methods.
Hu et al. (Fri,) studied this question.