Use AF-CNN for End-to-End Fiber Vibration Signal Recognition

Abstract

Traditional optical fiber vibration signal (OFVS) recognition research focuses on signal endpoint detection and feature extraction. These two aspects directly determine the success of OFVS recognition. The traditional method relies on artificially designed features and has a strong pertinence to the classification target, resulting in poor stability and flexibility. In response to the above problems, this paper combines the traditional OFVS recognition ideas (time-frequency analysis and feature extraction) and the characteristics of deep learning automatic learning parameters to construct an end-to-end adaptive filtering convolutional neural network (AF-CNN), which can directly get the classification results through the iterative update of the network. In modeling the original signal, the following steps were taken to make the network interpretable. First, we use a one-dimensional (1-D) convolution on the original OFVS. The convolution kernel can adaptively treat the original signal perform filtering to obtain filtered signals of different frequency bands. Second, using a general convolutional neural network (CNN) to extract the filtered signal features. Finally, a multi-layer perceptron (MLP) is used for classification. This paper compares the AF-CNN network with three traditional pattern recognition methods and proves that the AF-CNN network's accuracy is better than traditional pattern recognition methods. The average accuracy can reach 96.7%, and it can effectively distinguish OFVS with weaker energy and similar waveforms.