Fault prediction for aircraft control surface damage based on multi-factor high-order fuzzy variation

The trends of aircraft control system state parameters which can be measured are indirect manifestations of surface damage. In order to improve prediction accuracy, a method based on multi-factor high-order fuzzy variation is proposed. The proposed method constructs multi-cofactor fuzzy logical relationship with the variation of multivariate time series and extends the conventional fuzzy prediction method. The universe of discourse is divided into unequal interval length using self-organizing map (SOM) and its membership is reset. According to the periodic feature, a multi-factor high-order fuzzy variation model is built. In order to verify the validity of the method, the prediction and analysis of aileron fault trend was performed. The identification of fault type and fault degree is obtained by fault mapping model with the prediction results. Compared with the traditional method, the simulation result demonstrates the proposed prediction model has a better predictive ability.