A novel, unsupervised, hypersphere-based healthy subspace method for robust damage detection under non-quantifiable uncertainty via a limited number of random vibration response sensors is postulated. The method is based on the approximate construction, within a proper feature space, of a healthy subspace representing the healthy structural dynamics under uncertainty as the union of properly selected hyperspheres. This is achieved via a fully automated algorithm eliminating user intervention, and thus subjective selections, or complex optimization procedures. The main asset of the proposed method lies in combining simplicity and full automation with high performance. Its performance is systematically assessed via two experimental case studies featuring various uncertainty sources and distinct healthy subspace geometries, while interesting comparisons with three well-known robust damage detection methods are also performed. The results indicate excellent detection performance, which also compares favorably to that of alternative methods.

提出了一种新颖的，无监督的，基于超球面的健康子空间方法，用于通过有限数量的随机振动响应传感器在不可量化的不确定性下进行稳健的损伤检测。该方法基于在适当特征空间内的健康子空间的近似构造，该健康子空间代表不确定性下的健康结构动力学，作为正确选择的超球体的并集。这是通过一种完全自动化的算法来实现的，该算法消除了用户的干预，从而消除了主观选择或复杂的优化程序。所提出的方法的主要资产在于将简单性和完全自动化与高性能相结合。通过两个实验案例系统地评估了它的性能，这些案例研究涉及各种不确定性源和不同的健康子空间几何体，同时还与三种众所周知的鲁棒性损坏检测方法进行了有趣的比较。结果表明检测性能极佳，与其他方法相比也具有优势。