Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements†
AbstractNowadays, there is a strong demand for inspection systems integrating both high sensitivity under various testing conditions and advanced processing allowing automatic identification of the examined object state and detection of threats. This paper presents the possibility of utilization of a magnetic multi-sensor matrix transducer for characterization of defected areas in steel elements and a deep learning based algorithm for integration of data and final identification of the object state. The transducer allows sensing of a magnetic vector in a single location in different directions. Thus, it enables detecting and characterizing any material changes that affect magnetic properties regardless of their orientation in reference to the scanning direction. To assess the general application capability of the system, steel elements with rectangular-shaped artificial defects were used. First, a database was constructed considering numerical and measurements results. A finite element method was used to run a simulation process and provide transducer signal patterns for different defect arrangements. Next, the algorithm integrating responses of the transducer collected in a single position was applied, and a convolutional neural network was used for implementation of the material state evaluation model. Then, validation of the obtained model was carried out. In this paper, the procedure for updating the evaluated local state, referring to the neighboring area results, is presented. Finally, the results and future perspective are discussed. View Full-Text
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Psuj, G. Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements. Sensors 2018, 18, 292.
Psuj G. Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements. Sensors. 2018; 18(1):292.Chicago/Turabian Style
Psuj, Grzegorz. 2018. "Multi-Sensor Data Integration Using Deep Learning for Characterization of Defects in Steel Elements." Sensors 18, no. 1: 292.
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