Aircraft engine transient state fault detecting method

The invention provides an aircraft engine transient state fault detecting method which includes first extracting marked features from rotating speed data of an aircraft engine based on a Mahalanobis adaptive Hessian locally linear embedding feature extractor; then utilizing an adaptive BP neural network classifier to analyze and determine whether faults are generated in the transient state according to the result of the feature extraction; finally processing the detected faults through a diagnosis system to generate a maintenance guide. The method solves the problem that the existing aircraft fault detection is only confined to engine data collection in the steady state. The method adopts combination of the Hessian locally linear embedding (Mahalanobis adaptive Hessian locally linear embedding) and adaptive BP (Back Propagation) neural network. The method can effectively improve the fault detection performance of the aircraft engine in the transient state.