Configurable ultrasonic flaw classification of oil pipelines

To make a decision on the nature of a flaw within a pipeline, it is necessary to detect suspicious regions in ultrasonic inspection images reliably and to, derive geometrical parameters such as shape, position and orientation from each measured signal and finally to collate these parameters intelligently by associating each measured signal with a possible flaw type. These two papers present an innovative method of configurable flaw classification and volume estimation in oil pipelines. This method includes: (1) An adaptive thresholding selection for estimation of the area and volume of the flaws, (2) Dynamic detection of interesting points as feature points at different levels of images by wavelet transform, (3) A guided searching strategy for the best matching from the coarse level to a fine level with configurable classes of flaws, (4) A neural network error reducer using the area and volume of flaws and (5) A statistical error analyser to verify system outputs by distinct inputs. In part I, at first, the ultrasonic scan system acquires ultrasonic flaw signals. Then, the preprocessing module performs tasks such as filtering and smoothing. In addition, adaptive thresholding selection based on compactness measures of fuzzy S-functions is performed and the area and volume of flaws are estimated through a number of real images illustrating the performance of system. Comparing the proposed fuzzy edge detection method with the other well-known methods demonstrate the compatibility of approach in nondestructive ultrasonic inspection imaging. Flops calculation demonstrates that the proposed fuzzy estimator could be integrated into a real time flaw detection system. In part II, feature extractors such as wavelet transform, volume of the flaw, flaw classification and statistical error analysis are proposed, through acquired and preprocessed image.