Pattern Recognition Techniques Applied to the Detection and Classification of Welding Defects by Magnetic Testing

Abstract

The importance of nondestructive tests cannot be denied nowadays, and their use in the petroleum industry is extremely important to ensure safe operation of the equipment. The magnetic flux leakage (MFL) technique in instrumented pig is frequently used for pipeline inspection to detect weld defects and internal or external corrosion; however, as every nondestructive technique, it may present report errors, caused by several human factors (like lack of theoretical knowledge or experience in the technique applied, tiredness due to long hours of work, and so on) and by the inspection technique itself. Because pattern recognition techniques are already well known and applied in other technological research areas, such as fuzzy logic and artificial neural networks, the possibility to develop an automatic system to detect and classify defects through some nondestructive techniques aroused, mainly for those techniques that actually operate with signal or image interpretation. In the present work, pattern nonlinear classifiers were applied using artificial neural networks, to check the possibility to detect and classify defects in pipelines inspected through magnetic pigs (MFL technique). Several tests were performed on specimens with defects artificially inserted (internal and external corrosion and lack of penetration).