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Data-driven semi-supervised and supervised learning algorithms for health monitoring of pipes

机译：数据驱动的管道半监督与监督学习算法

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The use of guided ultrasonic waves (GUWs) for SHM of pipelines has been a popular method for over three decades. The superiority of GUWs over traditional vibration-based techniques lie in its ability to detect small damages (cracks and corrosion) over a satisfactory length of a pipeline. The physics of the system, however, is extremely involved that renders model-based techniques computationally prohibitive. Data-driven approaches, based on statistical learning algorithmsare far more suitable in such scenarios. In this paper, we propose two data-driven techniques, involving a semi-supervised and a supervised learning approach, for damage detection in pipes. In addition to circumventing the use of a model-based approach, the proposed approaches also aid in reducing the number of sensors deployed, leading to reductions in maintenance costs. The semi-supervised learning-based approach detects the presence of damage using a hierarchical clustering-based algorithm. The supervised learning-based approach performs damage localization in a multinomial logistic regression framework. We validate the proposed algorithms by acquiring guided wave responses from experimental pipes in a pitch-catch configuration using low-cost piezoelectric transducers. We demonstrate that our fully data-driven techniques accurately detect and localize cracks on two cast iron pipes of different lengths using a combination of two sensors. (C) 2019 Elsevier Ltd. All rights reserved.
机译：三十多年来，在管道的SHM中使用引导超声波（GUW）一直是一种流行的方法。与传统的基于振动的技术相比，GUW的优势在于它能够在令人满意的管道长度范围内检测到较小的损坏（裂纹和腐蚀）。但是，系统的物理过程极为复杂，这使得基于模型的技术无法进行计算。在这种情况下，基于统计学习算法的数据驱动方法更加合适。在本文中，我们提出了两种数据驱动技术，包括半监督和监督学习方法，用于管道中的损伤检测。除了规避基于模型的方法的使用之外，提出的方法还有助于减少部署的传感器数量，从而降低维护成本。基于半监督学习的方法使用基于层次聚类的算法来检测损坏的存在。基于监督学习的方法在多项逻辑回归框架中执行损伤定位。我们通过使用低成本压电换能器从节距捕获配置中的实验管道中获取导波响应来验证所提出的算法。我们证明了完全由数据驱动的技术使用两个传感器的组合，可以准确地检测和定位两条不同长度的铸铁管上的裂纹。 （C）2019 Elsevier Ltd.保留所有权利。

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