Measurement and quantification of defects are the key challenges for eddy current pulsed thermography (ECPT), localization and detection of surface, sub-surface defects and the quantification of the defects under complex geometry in particular. To address this issue, this paper proposed a 3D eddy current pulsed thermography system by integration the ECPT system with an RGB-D (3D point cloud with RGB texture information) camera. The thermal image is mapped to the RGB-D image via matched features from the thermal and visible image. In order to establish reliable key-point matches for multi-spectrum image registration, a new Shape Constrained Scale Invariant Feature Transform (SCSIFT) descriptor is proposed, which adds global shape information to SIFT descriptor. The experimental study has validated that the developed feature matching algorithm for the mapping of thermal image to RGB-D image. The newly developed 3D ECPT system provides quantified data including 3D heat distribution as well as the 3D spatial relationship between defect position, the excitation coil and the whole specimen, which shows its potential of quantitative evaluation of the defect characteristics.

The detection and quantification of the rolling contact fatigue (RCF) in rail tracks are essential for rail safety and condition-based maintenance. The tomographic reconstruction of the rolling contact fatigue is challenging work. The x-ray is unable to do in-situ inspection effectively. This paper proposes a new approach for RCF construction using 3D eddy current pulsed thermography. A differential time-square-root (sqrt) of temperature drop (DTSTD) is proposed as a mean to construct the sectional images and to reconstruct the thermal tomography image. The proposed method is validated through artificial angular crack slots as well as natural RCF crack. The thermal tomographic reconstruction is compared with the x-ray computed tomography on a rail track head cut-off with RCF cracks.

The quantitative non-destructive evaluation (QNDE) of complex defects remains as challenges. The transient thermal responses of 3D eddy current pulsed thermography (ECPT) contains rich information for the defect characteristics. In this paper, a modified skewness feature extracted from the transient thermal responses is proposed for the QNDE in 3D ECPT. Different stages of the transient responses and their skewness features are experimentally investigated for angular cracks. The skewness feature is mapped to 3D point cloud captured by a 3D ranger in order to show the 3D skewness feature distribution. The heating stage is modified and inverted before the calculation of the entire transient thermal response due to different behaviors of skewness features at different transient stages. Results show that the modified skewness removed the counteracting effect of the temperature distribution in the heating stage and cooling stage, which make the modified skewness feature has higher sensitivity and linearity for the evaluation of defect depths.

Railways play a pivotal role in national economic development, freight transportation, national defense, and regional connectivity. The detection of rolling contact fatigue (RCF) defects in rail tracks is essential for railway safety and maintenance. Due to its efficiency and non-contact capability in detecting surface and near-surface defects, Eddy Current Pulsed Thermography (ECPT) has garnered significant attention from researchers. However, detecting multi-layer RCF defects remains a challenge. This paper introduces a sweeping Eddy Current Pulsed Thermal-Tomography system (ECPTT) to detect multi-layer RCF defects effectively. This system utilizes varying excitation frequencies to heat defects, altering skin depth and facilitating feature extraction to distinguish multi-layer RCF defects. Skewness and thermographic signal reconstruction (TSR) values are employed as features in the experiments. These features are qualitatively analyzed to differentiate the layers and depths of multi-layer RCF defects. Additionally, five different coils were compared and analyzed quantitatively. The results indicate that the ECPTT system can effectively detect and distinguish multi-layer RCF defects, thereby providing more detailed defect information and enhancing railway safety and maintenance efficiency.