Research on the Early Warning and Broadcast of High-Pressure Turbine Thermal Corrosion Faults by Integrating Dynamic Simulation and Intelligent Identification
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摘要: 燃气轮机是空天和舰船装备的重要动力来源，涡轮作为燃-燃联合动力系统的关键部件，长时间工作在高温高压环境下，严苛的工作环境导致涡轮叶片易于遭受热腐蚀，从而可能引发系统级别的故障.因此，对涡轮进行热腐蚀故障诊断技术研究具有重要的工程意义.针对涡轮热腐蚀问题，提出了一种融合动态仿真和智能诊断算法的涡轮热腐蚀故障识别方法，利用模块化设计思路，在燃机运行机理数字化模型的基础上建立了整机动态仿真模型，通过标准差法检测所提取数据集中的异常值，使用KNN算法填补空缺值后，采用的小波包Bayes降噪使信号和数据更精准，然后根据人工智能算法构建了表征叶片热腐蚀受损的识别模型.最后，通过使用历史健康数据训练人工智能算法，依靠监测预警模型输出的预测值与实际测量值之间的偏差变化，实现了对涡轮热腐蚀故障的预警播报：在120台机组不同部件运行故障定位测试中，该方法故障精准识别率达95%；在24台机组不同数据特征下的高压涡轮热腐蚀故障预警测试中，故障预警准确率达91.7%以上.该研究拟为动力装备的数字化诊断提供技术参考.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.
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Key words:
- dynamic simulation /
- high-pressure turbine /
- hot corrosion /
- digitization /
- early warning of failure
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图 1 燃气轮机动态仿真模型
注 为了解释图中的颜色，读者可以参考本文的电子网页版本，后同.
Figure 1. The diagram of a dynamic simulation model for the gas turbine
图 2 燃-燃联合动力系统动态仿真模型
Figure 2. The dynamic simulation model diagram of the combined fuel-fuel power system
图 5 涡轮热腐蚀故障各部件影响情况
Figure 5. Reactions of various components of the compressor fouling failure system
图 8 涡轮热腐蚀预警模型
Figure 8. Schematic diagram of the early warning model for fouling in compressor blades
图 9 涡轮热腐蚀故障发生时故障识别模型对于各部件的响应
Figure 9. The responses of the fault identification model to each component during a compressor fouling fault
图 10 涡轮热腐蚀故障发生时部分故障识别模型对于各部件的响应
Figure 10. The response of the fault identification model to each component when a compressor fouling fault occur
parameter simulation value contrasting value deviation/% environmental temperature/K 288 288 0 environmental stress/kPa 101.325 101.325 0 power turbine speed/(r/min) 3 599 3 600 0.02 power turbine inlet temperature/K 1 034 1 041 0.7 power turbo expansion ratio 3.96 3.85 2.8 high-pressure turbine expansion ratio 4.59 4.54 1.1 
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表 2 动力系统各部件的报警准确度
Table 2. Alarm accuracy of each component of the power system
model type number of units number of false positives number of false negatives accuracy/% compressor model 24 0 0 100 combustion chamber model 24 1 0 95.8 high-pressure turbo model 24 2 0 91.7 power turbine model 24 1 1 91.7 gearbox model 24 0 0 100 propeller model 24 0 1 95.8
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