Open-Set Event Recognition Model Using 1-D RL-CNN With OpenMax Algorithm for Distributed Optical Fiber Vibration Sensing System
Zhengxian Zhou, Wenyang Jiao, Xing Hu, Dawei Zhang, Jun Qu, Xianfeng Zheng, Haima Yang, Songlin Zhuang
Abstract
Distributed optical fiber vibration sensing system (DVS) based on phase-sensitive optical time domain reflectometer (OTDR) is widely used for its simple structure and high sensitivity. Signal recognition is crucial for DVS because it can help to classify the different types of vibration events. Deep learning provides accurate event classification and can automatically extract features according to sample distribution. However, almost all current methods focus on closed-set recognition, which misclassifies unknown events into known categories, thus reducing the recognition accuracy of sensing system. In this article, we propose a novel open-set event recognition model based on 1-D residual learning convolution neural network (1-D RL-CNN) with OpenMax algorithm for DVS, which is capable of processing the signals of known and unknown categories. The experimental results show that the proposed recognition model improves the classification accuracy greatly compared with the conventional 1-D CNN signal classification method. The overall open-set classification accuracy of 1-D RL-CNN with OpenMax is 91.19%, which is improved by 18.47% and 7.57% compared with 1-D CNN with SoftMax and 1-D CNN with OpenMax.