Key Laboratory of Signal Detection and Processing, College of Information Science and Engineering, Xinjiang University, Urumqi 830017, China.
Sensors (Basel). 2022 Aug 12;22(16):6012. doi: 10.3390/s22166012.
Because of the problem of low recognition accuracy in the recognition of intrusion vibration events by the distributed Sagnac type optical fiber sensing system, this paper combines the traditional optical fiber vibration signal recognition idea and the characteristics of automatic feature extraction by a convolutional neural network (CNN) to construct a new endpoint detection algorithm and a method of fusing multiple-scale features CNN to recognize fiber vibration signals. Firstly, a new endpoint detection algorithm combining spectral centroid and energy spectral entropy product is used to detect the vibration part of the original signal, which is used to improve the detection effect of endpoint detection. Then, CNNs of different scales are used to extract the multi-level and multi-scale features of the signal. Aiming at the problem of information loss in the pooling process, a new method of combining differential pooling features is used. Finally, a multi-layer perceptron (MLP) is used to recognize the extracted features. Experiments show that the method has an average recognition accuracy rate of 98.75% for the four types of vibration signals. Compared with traditional EMD and VMD pattern recognition and 1D-CNN methods, the accuracy of the optical fiber vibration signal recognition is higher.
由于分布式萨格纳克型光纤传感系统在识别入侵振动事件时存在识别准确率低的问题，本文结合传统光纤振动信号识别思想和卷积神经网络（CNN）自动特征提取的特点，构建了一种新的端点检测算法和融合多尺度特征 CNN 的方法来识别光纤振动信号。首先，采用结合频谱质心和能量谱熵积的新端点检测算法来检测原始信号的振动部分，从而提高端点检测的检测效果。然后，使用不同尺度的 CNN 提取信号的多层次、多尺度特征。针对池化过程中信息丢失的问题，采用了一种新的差分池化特征融合方法。最后，使用多层感知机（MLP）对提取的特征进行识别。实验表明，该方法对四种振动信号的平均识别准确率为 98.75%。与传统的 EMD 和 VMD 模式识别以及 1D-CNN 方法相比，该光纤振动信号识别方法的准确率更高。
