Cable-stayed bridges are widely used all around the world. Unfortunately, during their service life, they are exposed to adverse conditions that may cause their deterioration and, consequently, their collapse. Vibration-based structural health monitoring techniques have become the most promising alternatives for efficiently detecting and locating damage into civil structures. In this regard, this paper presents a new methodology based on statistical features, Principal component analysis (PCA), and Mahalanobis distance (MD) for detecting and locating a cable loss in the Río Papaloapan bridge (RPB) using vibration signals. It is based on the extraction of a set of statistical time features (STFs) from vibration signals, which are analyzed using the autocorrelation function (ACF) to denoise and strengthen the features found in them. Then PCA-based models are computed by using the STFs to enhance the damage location process. Then a new damage index based on MD is proposed to indicate if a damage exists and its location.

中文翻译：

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基于统计特征、主成分分析和马氏距离的新损伤指数用于检测和定位斜拉桥中的电缆损耗

斜拉桥在世界各地广泛使用。不幸的是，在它们的使用寿命期间，它们暴露在可能导致它们劣化并因此而导致它们倒塌的不利条件下。基于振动的结构健康监测技术已成为有效检测和定位土木结构损坏的最有希望的替代方案。在这方面，本文提出了一种基于统计特征、主成分分析 (PCA) 和马氏距离 (MD) 的新方法，用于使用振动信号检测和定位 Río Papaloapan 桥 (RPB) 中的电缆损耗。它基于从振动信号中提取一组统计时间特征 (STF)，使用自相关函数 (ACF) 对其进行分析，以对其中发现的特征进行去噪和强化。然后使用 STF 计算基于 PCA 的模型，以增强损坏定位过程。然后提出了一种新的基于MD的损伤指数来指示损伤是否存在及其位置。