Identification of materials with magnetic characteristics by neural networks

• Published in: Measurement : journal of the International Measurement Confederation 2012-05, Vol.45 (4), p.734-744
• Main Authors: Nazlibilek, Sedat, Ege, Yavuz, Kalender, Osman, Sensoy, Mehmet Gökhan, Karacor, Deniz, Sazlı, Murat Hüsnü
• Format: Article
• Language: English
• Subjects: Anisotropic magnetoresistive sensor (AMR); Autonomous; Ferromagnetic materials; Magnetic anomaly; Magnetic materials; Mathematical analysis; Neural networks; Remote sensing; Sensors
• ISSN: 0263-2241; 1873-412X
• DOI: 10.1016/j.measurement.2011.12.017

► In work, remote sensing mechanism and autonomous identification system are given. ► The remote sensing mechanism utilizes KMZ51 anisotropic magneto-resistive sensor. ► The identification system is based on two kinds of neural network structures. ► MLP and RBF network types are used for training of the neural networks. In industry, there is a need for remote sensing and autonomous method for the identification of the ferromagnetic materials used. The system is desired to have the characteristics of improved accuracy and low power consumption. It must also autonomous and fast enough for the decision. In this work, the details of inaccurate and low power remote sensing mechanism and autonomous identification system are given. The remote sensing mechanism utilizes KMZ51 anisotropic magneto-resistive sensor with high sensitivity and low power consumption. The images and most appropriate mathematical curves and formulas for the magnetic anomalies created by the magnetic materials are obtained by 2-D motion of the sensor over the material. The contribution of the paper is the use of the images obtained by the measurement of the perpendicular component of the Earth magnetic field that is a new method for the purpose of identification of an unknown magnetic material. The identification system is based on two kinds of neural network structures. The MultiLayer Perceptron (MLP) and the Radial Basis Function (RBF) network types are used for training of the neural networks. In this work, 23 different materials such as SAE/AISI 1030, 1035, 1040, 1060, 4140 and 8260 are identified. Besides the ferromagnetic materials, three objects are also successfully identified. Two of them are anti-personal and anti-tank mines and one is an empty can box. It is shown that the identification system can also be used as a buried mine identification system. The neural networks are trained with images which are originally obtained by the remote sensing system and the system is operated by images with added Gaussian white noises.