Department of Applied Statistics, Operations Research and Quality, Universitat Politècnica de València, Camino de Vera s/n, 46022 Valencia, Spain.
Department of Natural Sciences and Mathematics, Pontificia Universidad Javeriana Cali, Cali 760031, Colombia.
Sensors (Basel). 2021 Jan 9;21(2):436. doi: 10.3390/s21020436.
The famous Renaissance frescoes in Valencia's Cathedral (Spain) have been kept under confined temperature and relative humidity () conditions for about 300 years, until the removal of the baroque vault covering them, carried out in 2006. In the interest of longer-term preservation and in order to maintain these frescoes in good condition, a unique monitoring system was implemented to record both air temperature and . Sensors were installed in different points at the vault of the apse, during the restoration process. The present study proposes a statistical methodology for analyzing a subset of data recorded in 2008 and 2010, from the sensors. This methodology is based on fitting different functions and models to the time series, in order to classify the sensors. The methodology proposed, computes and applies a discriminant technique to them. The correspond to estimates of parameters of the models and features such as mean and maximum, among others. These features are computed using values of the functions such as , , , and . The computed were structured as a matrix. Next, was applied in order to discriminate sensors according to their position in the vault. It was found that the classification of sensors derived from showed the best performance (i.e., lowest classification error rate). Based on these results, the methodology applied here can be useful for characterizing the differences in , measured at different positions in a historical building.
瓦伦西亚大教堂（西班牙）的著名文艺复兴时期壁画已经在限制的温度和相对湿度（）条件下保存了大约 300 年，直到 2006 年拆除了覆盖它们的巴洛克式拱顶。为了进行更长期的保护，并为了保持这些壁画的良好状态，实施了一个独特的监测系统，以记录空气温度和。在修复过程中，在拱顶的不同点安装了传感器。本研究提出了一种统计方法，用于分析 2008 年和 2010 年从传感器记录的一部分数据。该方法基于拟合不同的函数和模型到时间序列，以对传感器进行分类。所提出的方法计算了和，并对其应用了判别技术。是模型参数的估计值以及均值和最大值等特征。这些特征是使用函数的值计算得出的，例如、、、等。计算出的被构造为一个矩阵。然后，应用以根据拱顶中的位置对传感器进行分类。结果表明，来自的传感器分类表现出最佳性能（即，最低的分类错误率）。基于这些结果，应用的方法可用于表征在历史建筑的不同位置测量的之间的差异。
