摘要
事件识别是分布式光纤传感的重要应用,现有的模式识别手段存在泛化性差和对弱振动识别的准确率低两个主要难题。以马赫-曾德尔干涉仪为应用对象,从样本来源的角度改善传统的分类器,以时域信号经经验模态分解所得的本征模态函数构建训练样本,使用卷积算子提取信号的波形特征、频域特征、时频域特征,构建深度学习网络,并在相同的神经网络框架下以原始信号为输入设计了4个对照组。所提识别方案在测试集和验证集上对6种目标信号的准确率分别为97.02%和94.88%,泛化性和分类精度均处于最优状态。分类器的平均样本响应时间低于0.07 s,具备良好的可行性与发展前景。
Objective Threat event recognition is one of the widely researched topics in distributed fiber optic sensing.Deep learning is an important means for pattern recognition.The main challenges that limit its recognition accuracy can be categorized into two aspects:lack of generalization and existence of false recognition for some signals with low vibration intensity and obscure features.On one hand,this is due to ambiguous features,such that the target signal is often obscured in noise,and on the other hand,such signals are easily mislabeled in the process of constructing data sets.The classification accuracy of neural networks can be improved in three ways.The first approach is to preprocess the data from the front end of the network by applying various methods,such as band-pass filtering,wavelet denoising,and Hilbert transform.However,these methods have relatively limited positive effects and will contribute to the loss of detailed information to some extent.The second approach is to increase the extraction of features from training samples,such as inputting multiple features of the signal into the network simultaneously to improve the recognition accuracy through feature fusion.The third approach is to increase the means of feature extraction through various methodologies,such as increasing the number of convolutional layers,introducing recurrent neural networks(RNNs),and supplementing deep belief networks.The design of specific schemes should consider the data characteristics of the sensing system.In this study,the sampling rate and sampling points of the Mach-Zehnder interferometer(MZI)are 50 times greater than those of the phase-sensitive optical time-domain reflectometer.Thus,the third approach will substantially increase the computations and response time.In this paper,we attempted to implement the enhancement of vibration features from the perspective of signal sources for improving the recognition accuracy of weak vibration events under the traditional network framework.Methods The proposed recognition prog
作者
王鸣
封皓
沙洲
赵利
Wang Ming;Feng Hao;Sha Zhou;Zhao Li(State Key Laboratory of Precision Measuring Technology and Instruments,School of Precision Instrument and Opto-Electronics Engineering,Tianjin University,Tianjin 300072,China;Shandong Longquan Pipeline Engineering Co.,Ltd.,Zibo 255200,Shandong,China)
出处
《光学学报》
EI
CAS
CSCD
北大核心
2023年第19期85-95,共11页
Acta Optica Sinica
基金
国家自然科学基金(62005191,61873183)
广西自动检测技术及仪器重点实验室开放基金(YQ21205)。
关键词
深度学习
经验模态分解
马赫-曾德尔干涉仪
本征模态函数
事件识别
deep learning
empirical mode decomposition
Mach-Zehnder interferometer
intrinsic mode function
event recognition
