National Engineering Laboratory for Fiber Optic Sensing Technology, Wuhan University of Technology, Wuhan 430070, China.
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.
Sensors (Basel). 2020 Apr 12;20(8):2179. doi: 10.3390/s20082179.
Quantifying structural status and locating structural anomalies are critical to tracking and safeguarding the safety of long-distance underground structures. Given the dynamic and distributed monitoring capabilities of an ultra-weak fiber Bragg grating (FBG) array, this paper proposes a method combining the stacked denoising autoencoder (SDAE) network and the improved dynamic time wrapping (DTW) algorithm to quantify the similarity of vibration responses. To obtain the dimensionality reduction features that were conducive to distance measurement, the silhouette coefficient was adopted to evaluate the training efficacy of the SDAE network under different hyperparameter settings. To measure the distance based on the improved DTW algorithm, the one nearest neighbor (1-NN) classifier was utilized to search the best constraint bandwidth. Moreover, the study proposed that the performance of different distance metrics used to quantify similarity can be evaluated through the 1-NN classifier. Based on two one-dimensional time-series datasets from the University of California, Riverside (UCR) archives, the detailed implementation process for similarity measure was illustrated. In terms of feature extraction and distance measure of UCR datasets, the proposed integrated approach of similarity measure showed improved performance over other existing algorithms. Finally, the field-vibration responses of the track bed in the subway detected by the ultra-weak FBG array were collected to determine the similarity characteristics of structural vibration among different monitoring zones. The quantitative results indicated that the proposed method can effectively quantify and distinguish the vibration similarity related to the physical location of structures.
定量结构状态和定位结构异常对于跟踪和保障长距离地下结构的安全至关重要。鉴于超弱光纤布拉格光栅（FBG）阵列的动态和分布式监测能力，本文提出了一种结合堆叠去噪自编码器（SDAE）网络和改进的动态时间规整（DTW）算法的方法，用于量化振动响应的相似性。为了获得有利于距离测量的降维特征，采用轮廓系数来评估 SDAE 网络在不同超参数设置下的训练效果。为了基于改进的 DTW 算法进行距离测量，采用最近邻（1-NN）分类器搜索最佳约束带宽。此外，该研究还提出可以通过 1-NN 分类器评估用于量化相似性的不同距离度量的性能。基于来自加利福尼亚大学河滨分校（UCR）档案的两个一维时间序列数据集，详细说明了相似性度量的详细实现过程。在 UCR 数据集的特征提取和距离测量方面，所提出的相似性度量综合方法在性能上优于其他现有算法。最后，采集了超弱 FBG 阵列检测到的地铁轨道床的现场振动响应，以确定不同监测区域之间结构振动的相似特征。定量结果表明，该方法可以有效地量化和区分与结构物理位置相关的振动相似性。
