Fault detection of gas turbine air path system based on KPCA and DE

Abstract:

The state detection parameters of gas turbine gas-path components are extremely nonlinear and their fault characteristics are difficult to be extracted,using traditional KPCA for fault detection is difficult to scientifically value nuclear parameters, thus reducing the accuracy of fault detection.To solve this problem, this paper proposes a fault detection algorithm for kernel principal component analysis based on optimized hybrid kernel(DE-KPCA).Firstly, the dynamic weight hybrid kernel function is established, and the global and local mappings are optimized by adjusting the weight ratio of the kernel function. With the sample detection accuracy as the optimization target, the mixed core parameters were optimized successively.Finally, a principal component abnormal state detection method based on optimized hybrid kernel function is constructed to realize on-line detection of gas turbine gas-path faults.In this paper, the fault simulation of turbojet turbojet engine is verified, which proves that this method can realize the scientific value of nuclear parameters and is more accurate and practical for gas turbine gas-path fault detection than traditional KPCA detection.