In-line detection of defects in steel pipes using flexible GMR sensor array

Abstract

Steel pipes serve as the main source for transporting water, gas, and other petrochemical substances for longer distances. These pipes were able to withstand extreme weather conditions and hostile environments because of their remarkable properties such as higher strength, durability, lower cost, and improved wear and corrosion resistance. However, prolonged usage of these pipes in such environments may lead to the initiation of defects in their inner surface such as leak holes, cracks, corrosion, etc. In overtime, these defects may become more severe, resulting in component failure and property losses. Hence, earlier detection of defects is highly recommended to avoid these failures. In this work, an in-line robot system has been proposed for detecting the defects in the steel pipes. This robot utilizes a non-destructive way for evaluating the flaws by means of the magnetic flux leakage (MFL) technique. A 3D finite element model has been developed with the aid of ANSYS Maxwell 3D software for evaluating the generated magnetic field and optimizing the lift-off distance. The permanent magnet is preferred as the magnetizing material for implementing local magnetization in the inspection area. The magnetic flux leakage from the defect region is sensed by using a flexible GMR sensor array of six sensors. Artificial defects were introduced in a 6-inch diameter steel pipe in various shapes and the Arduino UNO controls the overall process. The data from the sensor array were collected using the Arduino and plotted as the waveform graph. From this graph, the voltage variations among the sensors represent the defect region. In addition, the higher peak in amplitude denotes that the flux is influenced by the defect’s depth. Thus, the waveform graph for the introduced defects was analyzed and all graph represents a better signal to noise ratio (SNR) for identifying the defects.