Autonomous corrosion detection in gas pipelines: a hybrid-fuzzy classifier approach using ultrasonic nondestructive evaluation protocols

Abstract

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.

Defects detection of gas pipeline near the welds based on self quotient image and discrete cosine transform

“…In general, if the radial MFL signals remained intact, defects can be detected by simple thresholding [12]. This paper puts the MFL signal to satisfy both positive Gauss threshold and negative Gauss threshold within a fixed region around welds to the detection criteria of the defects.…”

“…The study of Afzal focused on the de-noising technique and the noise reduction of MFL signals. Qidwai [12] researched on the defect detection and classification using the fuzzy time-frequency defect classifier. Ma [13] has applied the immune radial basis function neural networks (IRBFNN) for the defect detection.…”

Empirical Mode Decomposition And Rough Set Attribute Reduction For Ultrasonic Flaw Signal Classification

“…One is to find a transformation from the original feature variables to a lower-dimensional feature space. The most widely used method for ultrasonic flaw signal classification is principal components analysis (PCA) [11][12][13]. Another is to select a subset from the original features by some criterions, which is described in [3,14].…”