Novel Coil Transducer Induced Thermoacoustic Detection of Rail Internal Defects Towards Intelligent Processing

Abstract

A novel resonant tri-coil transducer is proposed to induce thermoacoustic (TA) signals for detecting rail internal defects. It consists of a ferrite plate-backed tri-coil and its associated circuits, a shielding layer, and a three-dimensional printed enclosure for supporting and securing. General TA detection theory is illustrated to understand how to detect rail internal defects. For achieving electromagnetic (EM) field shaping and focusing, the physical structure of the proposed transducer is optimized. It reduces backward EM fields and interference. Then, an equivalent circuit model between the proposed transducer and the rail is constructed to analyze optimal power transfer efficiency as a function of distance and frequency. To validate the design concept, the proposed transducer is fabricated for inducing TA signals, and experiments are performed. Several engineered rail specimens (UIC-60E) with different types of defects are prepared. The induced TA waves are found to be compressional. With selected features, a support vector machine algorithm-based multinomial classifier model is trained and employed for intelligent processing. Then, the proposed transducer is integrated into the self-designed automatic rail monitoring system. Experimental results show that the self-designed system can achieve an accuracy rate of 96.4%, successfully identifying defects in defect classification. The proposed transducer has low cost in fabrication and maintenance, focused magnetic fields with a large lift-off distance and enhanced acoustic wave intensity. Compared to earlier version of the system, the spacing between the transmitter and the rail has been further increased.