Time series analysis and novelty detection are effective and promising methods for data-driven structural health monitoring (SHM) based on the statistical pattern recognition paradigm. However, processing substantially large volumes of vibration measurements may represent a serious limitation, especially for long-term SHM programs of large-scale civil structures. Moreover, shortcomings like the choice of an appropriate time series model in an automatic manner, the determination of optimal orders of the identified model and the classification of random high-dimensional features for damage detection, can strongly affect the performance of these approaches. This study is intended to propose statistical pattern recognition methods regarding time series modeling for feature extraction and novelty detection in feature classification in the presence of big data. These methods include an automatic model identification algorithm, an improved order determination approach and a hybrid distance-based novelty detection through a combination of Partition-based Kullback-Leibler divergence and Mahalanobis-squared distance. Experimental datasets relevant to a cable-stayed bridge are considered to validate the effectiveness of the proposed methods. Results demonstrate that: the AutoRegressive-AutoRegressive with eXogenous input (AR-ARX) model turns out to be the most suitable representation for feature extraction; the orders of this model are efficiently and automatically determined; the proposed novelty detection approach is highly successful in detecting damage, even in case of large volumes of random high-dimensional features.

时间序列分析和新颖性检测是基于统计模式识别范例的数据驱动的结构健康监测（SHM）的有效且有前途的方法。但是，处理大量的振动测量值可能会带来严重的限制，尤其是对于大型民用建筑的长期SHM程序而言。此外，诸如以自动方式选择合适的时间序列模型，确定所识别模型的最佳阶数以及对损坏进行检测的随机高维特征分类之类的缺点会严重影响这些方法的性能。这项研究旨在提出有关时间序列建模的统计模式识别方法，用于在大数据存在下进行特征分类中的特征提取和新颖性检测。这些方法包括自动模型识别算法，改进的顺序确定方法以及通过基于分区的Kullback-Leibler发散和Mahalanobis平方距离的组合来进行基于距离的混合新奇检测。与斜拉桥有关的实验数据集被认为可以验证所提出方法的有效性。结果表明：具有外源输入的AutoRegressive-AutoRegressive模型（AR-ARX）证明是最适合特征提取的表示形式；该模型的顺序可以高效，自动地确定；