Fault diagnosis for the landing phase of the aircraft based on an adaptive kernel principal component analysis algorithm

Kernel principal component analysis is an effective fault diagnosis algorithm proved by large amount of practices in industry; however, kernel principal component analysis with constant parameters is unable to achieve satisfactory results when the working condition is dynamic. In this article, the parameters in the proposed kernel function are adaptively adjusted according to the maximum variance principle and the K-nearest neighbors approach. Because the variance of the data is maximized, the proposed adaptive kernel principal component analysis approach can obtain well diagnosis effect under the condition that only a small amount of data are available. Moreover, for the structure of the data is preserved by the K-nearest neighbors method, the rate of false alarm and underreporting is effectively reduced. In addition, a novel approach with a new algorithm for forgetting factor calculation is put forward so that the fault detection model can be updated and suits the actual situation better. Experiments on the landing phase of the aircraft have shown that adopting the proposed algorithm can detect faults better than using static model.