A multi-channel data-based fault diagnosis method integrating deep learning strategy for aircraft sensor system

Abstract

The effectiveness and safety of an aircraft’s flight depend heavily on the flight control system. Since the attitude sensor is the weakest link, identifying its failure modes is crucial. To overcome the shortcomings of a single diagnosis model and a single input signal, this paper proposes a hybrid deep fault diagnosis model based on multi-data fusion. First, the normal and fault models of the sensor are established, and the residual timing signals of the sensor in different fault states are obtained. The frequency domain and timefrequency domain representations of the original timing signals are collected by means of fast Fourier transform and S-transform, and they are used as the input of the hybrid deep diagnosis model. The deep model is designed for the three inputs to mine the characteristics of the input data. These three deep features are concatenated and dimensionally reduced to obtain more comprehensive and representative features. Finally, the classifier is used to classify and obtain the diagnosis results. Through experiments, the advantages of the proposed method are verified by comparing it with several other methods.