Structural Health Monitoring ( IF 6.6 ) Pub Date : 2021-03-24 , DOI: 10.1177/1475921721998476 William Soo Lon Wah 1 , Yining Xia 2
Damage detection methods developed in the literature are affected by the presence of outlier measurements. These measurements can prevent small levels of damage to be detected. Therefore, a method to eliminate the effects of outlier measurements is proposed in this article. The method uses the difference in fits to examine how deleting an observation affects the predicted value of a model. This allows the observations that have a large influence on the model created, to be identified. These observations are the outlier measurements and they are eliminated from the database before the application of damage detection methods. Eliminating the outliers before the application of damage detection methods allows the normal procedures to detect damage, to be implemented. A multiple-regression-based damage detection method, which uses the natural frequencies as both the independent and dependent variables, is also developed in this article. A beam structure model and an experimental wooden bridge structure are analysed using the multiple-regression-based damage detection method with and without the application of the method proposed to eliminate the effects of outliers. The results obtained demonstrate that smaller levels of damage can be detected when the effects of outlier measurements are eliminated using the method proposed in this article.
中文翻译:
消除了在变化的环境条件下对结构进行损伤检测的异常值测量
文献中开发的损伤检测方法受异常值测量的影响。这些测量可以防止检测到较小程度的损坏。因此,本文提出了一种消除异常值测量影响的方法。该方法使用拟合差异来检查删除观察值如何影响模型的预测值。这样可以识别对创建的模型有很大影响的观察结果。这些观测值是异常测量值,在应用损害检测方法之前将其从数据库中删除。在应用损坏检测方法之前消除异常值可以执行检测损坏的正常过程。基于多元回归的损伤检测方法,本文还开发了使用固有频率作为自变量和因变量的方法。使用基于多重回归的损伤检测方法(不使用或不使用建议的方法来消除异常值的影响)来分析梁结构模型和实验性木桥结构。获得的结果表明,使用本文提出的方法消除离群值测量的影响后,可以检测到较小程度的损坏。