A Novel Method for Gas Turbine Condition Monitoring Based on KPCA and Analysis of Statistics T² and SPE

• Main Authors: Li Zeng, Wei Long, Yanyan Li
• Format: Article
• Language: English
• Published: MDPI AG 2019-02-01
• Series: Processes
• Subjects: KPCA; T2 statistical model; SPE statistical model; kernel function
• Online Access: https://www.mdpi.com/2227-9717/7/3/124

Gas turbines are widely used all over the world, in order to ensure the normal operation of gas turbines, it is necessary to monitor the condition of gas turbine and analyze the tested parameters to find the state information contained in parameters. There is a problem in gas turbine condition monitoring that how to locate the fault accurately if failure occurs. To solve the problem, this paper proposes a method to locate the fault of gas turbine components by evaluating the sensitivity of tested parameters to fault. Firstly, the tested parameters are decomposed by the kernel principal component analysis. Then construct the statistics of T² and SPE in the principal elements space and residual space, respectively. Furthermore, the thresholds of the statistics must be calculated. The influence of tested parameters on faults is analyzed, and the degree of influence is quantified. The fault location can be realized according to the analysis results. The research results show that the proposed method can realize fault diagnosis and location accurately.