Department of Computer Science & Engineering, Qatar University, Doha, Qatar.
IEEE Trans Ultrason Ferroelectr Freq Control. 2009 Dec;56(12):2650-65. doi: 10.1109/TUFFC.2009.1356.
In this paper, a customized classifier is presented for the industry-practiced nondestructive evaluation (NDE) protocols using a hybrid-fuzzy inference system (FIS) to classify the corrosion and distinguish it from the geometric defects or normal/healthy state of the steel pipes used in the gas/petroleum industry. The presented system is hybrid in the sense that it utilizes both soft computing through fuzzy set theory, as well as conventional parametric modeling through H(infinity) optimization methods. Due to significant uncertainty in the power spectral density of the noise in ultrasonic NDE procedures, the use of optimal H(2) estimators for defect characterization is not so accurate. A more appropriate criterion is the H(infinity) norm of the estimation error spectrum which is based on minimization of the magnitude of this spectrum and hence produces more robust estimates. A hybrid feature set is developed in this work that corresponds to a) geometric features extracted directly from the raw ultrasonic A-scan data (which are the ultrasonic echo pulses in 1-Dtraveling inside the metal perpendicular to its 2 surfaces) and b) mapped features from the impulse response of the estimated model of the defect waveform under study. An experimental strategy is first outlined, through which the necessary data are collected as A-scans. Then, using the H(infinity) estimation approach, a parametric transfer function is obtained for each pulse. In this respect, each A-scan is treated as output from a defining function when a pure/healthy metal's A-scan is used as its input. Three defining states are considered in the paper; healthy, corroded, and defective, where the defective class represents metal with artificial or other defects. The necessary features are then calculated and are then supplied to the fuzzy inference system as input to be used in the classification. The resulting system has shown excellent corrosion classification with very low misclassification and false alarm rates.
本文提出了一种定制的分类器，用于使用混合模糊推理系统（FIS）对工业实践中的无损评估（NDE）协议进行分类，以对腐蚀进行分类，并将其与用于天然气/石油工业的钢管的几何缺陷或正常/健康状态区分开来。所提出的系统是混合的，因为它既利用了模糊集理论的软计算，又利用了 H(infinity)优化方法的传统参数建模。由于超声 NDE 过程中噪声的功率谱密度存在很大的不确定性，因此使用最优 H(2)估计器进行缺陷特征描述并不那么准确。更合适的标准是估计误差谱的 H(infinity)范数，它基于该谱的幅度最小化，因此产生更稳健的估计。在这项工作中开发了一个混合特征集，对应于 a）直接从原始超声 A 扫描数据中提取的几何特征（这是在金属中垂直于其 2 个表面的 1-D 传播的超声回波脉冲）和 b）从所研究的缺陷波形估计模型的脉冲响应映射的特征。首先概述了一个实验策略，通过该策略收集作为 A 扫描的必要数据。然后，使用 H(infinity)估计方法，为每个脉冲获得一个参数传递函数。在这方面，当使用纯净/健康金属的 A 扫描作为其输入时，每个 A 扫描都被视为定义函数的输出。本文考虑了三种定义状态；健康、腐蚀和缺陷，其中缺陷类表示具有人工或其他缺陷的金属。然后计算必要的特征，并将其作为输入提供给模糊推理系统，以便在分类中使用。所得到的系统表现出出色的腐蚀分类，误分类和误报率非常低。
