| description abstract | As a crucial section of gas turbine maintenance decisionmaking process, to date, gas path fault diagnostic has gained a lot of attention. However, modelbased diagnostic methods, like nonlinear gas path analysis (GPA) and genetic algorithms, need an accurate gas turbine model, and diagnostic methods without gas turbine model, like expert system, need a knowledge database. Both are difficult to gain. Thus, datadriven approach for gas path diagnosis, like artificial neural network, is increasingly attractive. Support vector machine (SVM), a novel computational learning method, seems to be a good choice for datadriven gas path fault diagnosis of gas turbine. In this paper, SVM is employed to diagnose a deteriorated gas turbine. The effect of sample number, kernel function, and monitoring parameters on diagnostic accuracy are studied, respectively. Additionally, the diagnostic result of SVM is compared to the result of artificial neural networks. The comparing result confirms that SVM has an obvious advantage over artificial neural networks method based on a small sample of data and can be employed to gas path fault diagnosis of gas turbine. In addition, SVM with radial basis kernel function is the best choice for gas turbine gas path fault diagnosis based on small sample. | |
