Research on Feature Extraction Method for Fault Prediction of Avionics

Feature extraction is the key technique for fault prediction of avionics, Wavelet transform, Fourier transform, empirical mode decomposition methods can be used to extract fault features of the electronic equipment with few test points. Due to the fact that the avionics is large-scale integrated circuits which includes many test points, fault features extracted based on the method above may be mixed with each other and the number is large, which will seriously affect the accuracy and speed of fault prediction. It is a difficult problem to extract fault features from many fault information. In this paper, we propose the method based on denoising autoencoder and maximum likelihood to extract fault features from a large number of fault information. First of all, maximum likelihood is taken to analyze the high dimensional data comprised of the fault information which were extracted from many test points and historical degradation process, and to estimate the intrinsic dimension of fault features; Then, the high dimensional data is mapped to the specified dimension data space by using denoising autoencoder method. The key fault features are extracted from the data, and the redundant information is removed. Finally, taking the avionics power system as an example, through the fault feature visualization and health assessment demonstrate that the method proposed in the paper which can extract fault features is effective.