Fault detection for turbine engine disk based on an adaptive kernel principal component analysis algorithm

Abstract

For commercial aircraft, real-time fault detection is essential for condition monitoring of rotating engine components, which can improve aviation safety and reduce maintenance cost for airline companies. In this article, based on the adaptive kernel principal component analysis method, a real-time fault detection algorithm is proposed for turbine engine disk condition monitoring. A sample reduction strategy based on the k-nearest neighbors method is presented to speed up the kernel principal component analysis approach while still guaranteeing correct results. To efficiently detect fault, the fault detection model is updated timely to suit the working process of turbine engine disk. Sample clusters are obtained through the k-mean method, and the parameter of the kernel function is adaptively adjusted by minimizing the within-cluster distance and maximizing the between-cluster distance in the feature space. Experiments have demonstrated the superiority of the proposed approach in fault detection for turbine engine disk.