Abstract
Gas turbine hot component failures often cause catastrophic consequences. Fault detection can improve the availability and economy of hot components. The exhaust gas temperature (EGT) profile is usually used to monitor the performance of the hot components. The EGT profile is uniform when the hot component is healthy, whereas hot component faults lead to large temperature differences between different EGT values. The EGT profile swirl under different operating and ambient conditions also cause temperature differences. Therefore, the influence of EGT profile swirl on EGT values must be eliminated. To improve the detection sensitivity, this paper develops a fault detection method for hot components based on a convolutional neural network (CNN). This paper demonstrates that a CNN can extract the information between adjacent EGT values and consider the impact of the EGT profile swirl. This paper reveals, in principle, that a CNN is a viable solution for dealing with fault detection for hot components. Based on the distribution characteristics of EGT thermocouples, the circular padding method is developed in the CNN. The sensitivity of the developed method is verified by real-world data. Moreover, the developed method is visualized in detail. The visualization results reveal that the CNN effectively considers the influence of the EGT profile swirl.
Highlights
Gas turbines are widely used as the main power source in many areas, such as aircraft, ships, oil and gas applications, and power generation
To evaluate the effectiveness of the convolutional neural network (CNN) for hot component fault detection, detection performance waswas compared between the artificial neural network (ANN), parameters the detection performance compared between the ANN,extreme learning machines (ELM), ELM,and andCNN
It is found that the abnormal information is contained in several adjacent exhaust gas temperature (EGT) values rather than the global EGT profile, and the EGT profile swirl reflects the shift of some key features
Summary
Gas turbines are widely used as the main power source in many areas, such as aircraft, ships, oil and gas applications, and power generation. Many researchers have focused on the fault detection method of the hot components based on the EGT model [11,12,13,14,15,16,17,18,19,20,21]. Liu et al [21] presented an EGT model and two main factors affecting the EGT were considered, including the operating and ambient conditions, and the structure deviation of different combustors caused by processing and installation errors. Energies 2018, 11,gas x turbine hot components, the EGT model should be developed by considering. The influence of profile swirl on fault detection of the hot components is described. The reason why profile swirl on fault detection of the hot components is described. Based on the distribution characteristics of gas turbine for hot component fault detection.
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