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计算机系统应用:2020,29(1):144-150
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基于一维堆叠卷积自编码器的分布式应变裂缝检测
(长安大学 信息工程学院, 西安 710064)
Distributed Strain Crack Detection Based on One-Dimensional Stacked Convolutional Autoencoder
(School of Information Engineering, Chang'an University, Xi'an 710064, China)
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投稿时间:2019-06-24    修订日期:2019-07-16
中文摘要: 桥梁裂缝检测对于桥梁健康检测具有重要的意义.基于布里渊时域分析的分布式光纤传感器能够测量整个结构表面的应变数据.由于测量所得应变数据信噪比低,存在裂缝损伤处的应变异常被噪声“淹没”和“混淆”的问题.针对这一问题,提出一种基于一维堆叠卷积自编码器的分类检测方法.该方法具有噪声鲁棒性强、自提取特征可判别性高等优势.首先,通过布置光纤传感器获取结构表面应变数据,对光纤应变数据进行标准化预处理,并划分应变子序列.然后,使用一维堆叠卷积自编码器自动提取应变子序列的特征.最后,通过Softmax分类器对所提取的应变子序列特征进行分类,即裂缝或非裂缝.实验结果表明,该方法可以有效检测微小裂缝,检测准确率高.并且该方法提取的特征可判别性优于卷积神经网络和堆叠自编码器等方法.
Abstract:Bridge crack detection has great significance for bridge condition monitoring. Distributed fiber optic sensors are widely used to detect bridge cracks. The state quo approach is based on Brillouin time domain analysis. Though being capable of measuring strain data across the surface of the structure, it has its own flaw—the strain anomaly at the crack damage is “submerged” and “confused” by the noise due to the lower signal-to-noise ratio of the measured strain data. To this end, we propose a classification detection method based on one-dimensional stacked convolution autoencoder, which are endowed with strong noise robustness and high resolution of auto extraction features. The proposed method consists of three steps. First, structural surface strain data is acquired by arranging fiber optic sensors, the fiber strain data preprocessed and the strain subsequence divided. Second, the characteristics of the strain subsequence are automatically extracted using a one-dimensional stacked convolution autoencoder. Finally, the extracted strain subsequence features are classified by a Softmax classifier into two categories—cracks or non-cracks. The method can effectively detect micro cracks and has high detection accuracy. Moreover, by experimental contrast we claim that the feature discriminability extracted by this method is better than that of convolutional neural network and stacking autoencoder.
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严国萍,陈禹,李雨冲,闫昭帆.基于一维堆叠卷积自编码器的分布式应变裂缝检测.计算机系统应用,2020,29(1):144-150
YAN Guo-Ping,CHEN Yu,LI Yu-Chong,YAN Zhao-Fan.Distributed Strain Crack Detection Based on One-Dimensional Stacked Convolutional Autoencoder.COMPUTER SYSTEMS APPLICATIONS,2020,29(1):144-150

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