基于无监督更深层FCDD的乡村铁路枕木劣化检测用于预测性维护
Wooden Sleeper Deterioration Detection for Rural Railway Prognostics Using Unsupervised Deeper FCDDs
AI总结：
本文提出基于更深层FCDD的无监督单类分类流水线，用于乡村铁路枕木劣化检测，并通过消融实验和特征可视化验证其在铁路预测性维护中的可行性。
摘要：
在日常铁路运营中保持高标准的用户安全对铁路管理者至关重要。为辅助这一工作，顶部或侧视摄像头以及GPS定位系统已推动了对缺陷特征进行自动化定期检查以及评估铁路部件劣化状态的进展。然而，由于劣化状态在时间上出现极度不平衡，收集劣化状态数据可能非常耗时，并且需要重复采集数据。在监督学习中，需要数千个包含缺陷原始图像和标注标签的成对数据集。相比之下，单类分类方法具有优势，只需较少图像即可优化用于训练正常和异常特征的参数。更深层全卷积数据描述（FCDD）已适用于结构中混凝土/钢构件的多个损伤数据集，以及灾害中的倒木和木结构建筑倒塌。然而，目前尚不清楚其是否适用于铁路部件。在本研究中，我们设计了一个预测性判别器流水线，利用更深层FCDD对缺陷铁路部件进行自动化单类损伤分类。我们还对基于卷积神经网络（CNN）的更深层骨干网络进行了消融研究。此外，我们通过使用转置高斯上采样对劣化特征进行了可视化。我们利用在阴天和晴天场景下从后视角度采集的铁路轨道视频数据集，展示了该方法在铁路检查中的应用。最后，我们检验了该方法在预测性维护中的可用性，并讨论了铁路检查的未来工作。
英文摘要：
Maintaining high standards for user safety during daily railway operations is crucial for railway managers. To aid in this endeavor, top- or side-view cameras and GPS positioning systems have facilitated progress toward automating periodic inspections of defective features and assessing the deteriorating status of railway components. However, collecting data on deteriorated status can be time-consuming and requires repeated data acquisition because of the extreme temporal occurrence imbalance. In supervised learning, thousands of paired data sets containing defective raw images and annotated labels are required. However, the one-class classification approach offers the advantage of requiring fewer images to optimize parameters for training normal and anomalous features. The deeper fully-convolutional data descriptions (FCDDs) were applicable to several damage data sets of concrete/steel components in structures, and fallen tree, and wooden building collapse in disasters. However, it is not yet known to feasible to railway components. In this study, we devised a prognostic discriminator pipeline to automate one-class damage classification using the deeper FCDDs for defective railway components. We also performed ablation studies of the deeper backbone based on convolutional neural networks (CNNs). Furthermore, we visualized deterioration features by using transposed Gaussian upsampling. We demonstrated our application to railway inspection using a video acquisition dataset of railway track from backward view at a cloudy and sunny scene. Finally, we examined the usability of our approach for prognostics and future work on railway inspection.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
