Abstract Development of a method for distributed detection of microcracks on structural elements with very small crack opening displacements is described in this study. Robust principal component analysis (RPCA) and support vector machine (SVM) techniques were employed for denoising and classification of the signals. The objective was to detect microcracks in structural elements less than 30 µm in size. The viability of the method was accomplished by experiments involving a 15-meter steel beam with known microcracks. A distributed optical fiber sensor system based on the Brillouin scattering technology was employed for distributed measurement of strains along the length of the 15-m long beam. Distributed strain signals based on Brillouin based sensors possess inherent system noise and ambient perturbations which in turn reduce the signal-to-noise ratio of the measurements. Therefore, it is not possible to detect the smaller microcracks with small crack opening displacements. Smaller CODs are lost within the noisy distributed strain signal acquired by the Brillouin system. Undetected microcracks result in larger cracks, corrosion, and other anomalies with severe economical and safety ramifications. The method introduced for denoising and enhancement of the signal in the present study enables manifestation of the singularities on the distributed strain data and detection of microcracks. The significant component containing those singularities is effectively separated from the noise component by RPCA-based matrix decomposition. An SVM classifier with Gaussian kernel function was designed, through which the crack detections are realized by singular and nonsingular binary classification. The experimental results demonstrated that it was possible to detect microcracks with CODs as low as 23 µm without errors.

中文翻译：

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用于分布式光纤传感器微裂纹检测的鲁棒主成分分析和支持向量机

摘要 本研究描述了一种用于分布式检测裂纹张开位移很小的结构元件上的微裂纹的方法的开发。稳健的主成分分析 (RPCA) 和支持向量机 (SVM) 技术被用于对信号进行去噪和分类。目的是检测尺寸小于 30 µm 的结构元件中的微裂纹。该方法的可行性是通过涉及具有已知微裂纹的 15 米钢梁的实验完成的。基于布里渊散射技术的分布式光纤传感器系统用于沿 15 米长光束长度的应变的分布式测量。基于布里渊传感器的分布式应变信号具有固有的系统噪声和环境扰动，这反过来会降低测量的信噪比。因此，不可能用小的裂纹张开位移来检测更小的微裂纹。较小的 COD 在布里渊系统获取的噪声分布应变信号中丢失。未检测到的微裂纹会导致更大的裂纹、腐蚀和其他异常情况，从而对经济和安全产生严重的影响。本研究中引入的用于信号去噪和增强的方法能够在分布式应变数据上表现奇异性并检测微裂纹。通过基于 RPCA 的矩阵分解，包含这些奇异点的重要成分与噪声成分有效分离。设计了一种具有高斯核函数的SVM分类器，通过奇异和非奇异二元分类实现裂纹检测。实验结果表明，可以无误地检测 COD 低至 23 µm 的微裂纹。