3D Anisotropic Magnetoresistance sensor for steel health monitoring

Abstract

Electromagnetic sensors are widely used for safety and maintenance inspection purposes of ferromagnetic materials used in aerospace, construction and transport industry. In this paper, a portable device based on an Anisotropic Magnetoresistance (AMR) sensor for steel health monitoring is presented. The principle of operation of this device is the detection of magnetic anomalies of ferromagnetic materials, such as steel, due to stresses, corrosion, or other kind of defections. For this purpose, a cost-effective, low-consumption and high-sensitivity device has been developed, consisting of a sensor, a magnetic yoke and a microcontroller. The AMR sensor, is placed above the material under test, giving the ability of contactless measurement of the magnetic flux density on 3 axes. Additionally, a yoke, consisting of two iron poles and a permanent magnet between them, is used in this implementation, in order to create a closed magnetic circuit with the steel sample, enhancing the measurements. Then, a microcontroller with the appropriate electronic components receives the acquired data, enabling their real-time analysis and visualization of the measurements through a personal computer, smartphone, etc. Moreover, a 3D-printed enclosure has been designed and constructed, which both protects the sensor and maintains the desired position and alignment with the samples. The device can be portable due to an integrated Li-Po battery and its Wi-Fi capabilities. It can be used to determine the location of the defective spots or zones of the material under inspection, facilitating its treatment through rehabilitation methods that can be applied on the affected area. Both single-point and scanning mode monitoring are possible, making it suitable for many testing scenarios. Furthermore, its ease of use and portability render it accessible to a wide range of users, and appropriate for on-site measurements. The initial measurements using the device on test samples, demonstrate its reliability and accuracy in steel health monitoring.