Method to enhance time series rolling fault prediction by deep fast Fourier convolution

Abstract

Deep learning realizes the adaptive extraction of features in remaining useful life (RUL), and most of the methods combined with traditional signal processing stay in the preprocessing stage. A fast Fourier convolutional gated recurrent unit (FFCGRU) method is proposed, which incorporates fast Fourier transform (FFT) into a convolutional neural network for adaptive feature extraction, enhancing fault prediction. First, a plug-and-play FFT convolution block is proposed. This method combines signal processing with the adaptive deep learning process and achieves a larger receptive field through a smaller convolution kernel. Secondly, an adaptive pooling layer is designed before the feature map is input into the gated recurrent unit. The feature maps of different channels are aggregated into one feature value to reduce the redundant information of the signal feature map. Comparative studies indicate that the FFCGRU method utilizes a small number of parameters even with large convolutional kernels and exhibits robustness.