Research on the Early Warning and Broadcast of High-Pressure Turbine Thermal Corrosion Faults by Integrating Dynamic Simulation and Intelligent Identification

XU Zhe; CHEN Yanmu; ZHAO Haixin; CHEN Xudong; LU Yeming

doi:10.21656/1000-0887.450270

Volume 46 Issue 8

Aug. 2025

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Article Navigation > Applied Mathematics and Mechanics > 2025 > 46(8): 999-1015

XU Zhe, CHEN Yanmu, ZHAO Haixin, CHEN Xudong, LU Yeming. Research on the Early Warning and Broadcast of High-Pressure Turbine Thermal Corrosion Faults by Integrating Dynamic Simulation and Intelligent Identification[J]. Applied Mathematics and Mechanics, 2025, 46(8): 999-1015. doi: 10.21656/1000-0887.450270

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Research on the Early Warning and Broadcast of High-Pressure Turbine Thermal Corrosion Faults by Integrating Dynamic Simulation and Intelligent Identification

doi: 10.21656/1000-0887.450270

1. The 92942 Unit of PLA, Beijing 100161, P.R.China;
2. School of Energy and Power Engineering, Dalian University of Technology, Dalian, Liaoning 116024, P.R.China;
3. Power Station Material Technology Department, Xi’an Thermal Power Research Institute Co., Ltd., Xi’an 710054, P.R.China

Funds:

The National Science Foundation of China(52475241)

Received Date: 2024-10-08
Rev Recd Date: 2025-06-22

Available Online: 2025-09-10

Abstract

Abstract

Gas turbines are important power sources for aerospace and marine equipment. As a key part of the combined combustion system, turbines operating in high-temperature, high-pressure environments for extended periods are prone to blade thermal corrosion, which can lead to system failures. Therefore, researching the thermal corrosion fault diagnosis technology for turbines is of great engineering importance. To address turbine thermal corrosion, a method integrating dynamic simulation and intelligent diagnostic algorithms was proposed. A dynamic simulation model was established based on the engine's operating mechanism with a modular design approach. The outliers were detected with the standard deviation method and the missing values were filled with the KNN algorithm. The wavelet packet Bayesian denoising was then used to obtain precise signals and data, to enable the construction of an identification model for thermal corrosion damage of the blades with artificial intelligence algorithms. By means of training artificial intelligence algorithms with historical health data and monitoring the deviations between predicted values and actual measurements from the early warning model, the early detection of turbine thermal corrosion faults was realized. The testing results of different components of 120 units show that, the proposed method has a precise fault-localizing identification rate of 95%. The high-pressure turbine thermal corrosion fault early warning tests with different data characteristics in 24 units give an accuracy above 91.7%. This study provides technical references for the digitized diagnosis of power equipment.
- dynamic simulation,
- high-pressure turbine,
- hot corrosion,
- digitization,
- early warning of failure

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References(17)

References

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