National Engineering Research Center of Fiber Optic Sensing Technology and Networks, Wuhan University of Technology, Wuhan 430070, China.
School of Information Engineering, Wuhan University of Technology, Wuhan 430070, China.
Sensors (Basel). 2022 Jul 28;22(15):5653. doi: 10.3390/s22155653.
Changes in the geological environment and track wear, and deterioration of train bogies may lead to the looseness of subway fasteners. Identifying loose fasteners randomly distributed along the subway line is of great significance to avoid train derailment. This paper presents a convolutional autoencoder (CAE) network-based method for identifying fastener loosening features from the distributed vibration responses of track beds detected by an ultra-weak fiber Bragg grating sensing array. For an actual subway tunnel monitoring system, a field experiment used to collect the samples of fastener looseness was designed and implemented, where a crowbar was used to loosen or tighten three pairs of fasteners symmetrical on both sides of the track within the common track bed area and the moving load of a rail inspection vehicle was employed to generate 12 groups of distributed vibration signals of the track bed. The original vibration signals obtained from the on-site test were converted into two-dimensional images through the pseudo-Hilbert scan to facilitate the proposed two-stage CAE network with acceptable capabilities in feature extraction and recognition. The performance of the proposed methodology was quantified by accuracy, precision, recall, and F1-score, and displayed intuitively by t-distributed stochastic neighbor embedding (t-SNE). The raster scan and the Hilbert scan were selected to compare with the pseudo-Hilbert scan under a similar CAE network architecture. The identification performance results represented by the four quantification indicators (accuracy, precision, recall, and F1-score) based on the scan strategy in this paper were at least 23.8%, 9.5%, 20.0%, and 21.1% higher than those of the two common scan methods. As well as that, the clustering visualization by t-SNE further verified that the proposed approach had a stronger ability in distinguishing the feature of fastener looseness.
地质环境和轨道磨损的变化，以及转向架的恶化，可能导致地铁扣件松动。识别沿地铁线路随机分布的松动扣件对于避免列车脱轨具有重要意义。本文提出了一种基于卷积自动编码器（CAE）网络的方法，用于从超弱光纤布拉格光栅传感阵列检测到的轨道床分布式振动响应中识别扣件松动特征。针对实际的地铁隧道监测系统，设计并实施了一个现场实验，该实验使用撬棍松开或拧紧轨道床公共区域两侧轨道上的三对扣件，同时使用轨道检查车的移动荷载产生 12 组轨道床分布式振动信号。从现场测试获得的原始振动信号通过伪希尔伯特扫描转换为二维图像，便于提出的具有可接受特征提取和识别能力的两阶段 CAE 网络使用。通过准确性、精度、召回率和 F1 分数来量化所提出方法的性能，并通过 t 分布随机邻居嵌入（t-SNE）直观地显示。在类似的 CAE 网络架构下，选择栅格扫描和希尔伯特扫描与伪希尔伯特扫描进行比较。基于本文中扫描策略的四个量化指标（准确性、精度、召回率和 F1 分数）表示的识别性能结果至少比两种常见扫描方法高 23.8%、9.5%、20.0%和 21.1%。此外，t-SNE 的聚类可视化进一步验证了所提出的方法在区分扣件松动特征方面具有更强的能力。
