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Active three-dimensional(3-D) thermography combines 3-D shape and active thermography. Thus, it enables the provision of the intuitive thermographic inspection results for composite parts with a complex geometry. However, conventional 3-D thermography acquires thermographic information and 3-D shape through at least two independent sensors and requires complex cross-modal image registration algorithm based on the keypoint detection and matching. In this article, an active 3-D thermography system is proposed using only one thermal camera for moving objects. This system does not require an independent 3-D sensor while the thermal camera acts as the 3-D sensor. Nature behind that is that a mathematical model is proposed to unify the line scanning thermography and laser triangulation in the dynamic scanning process, taking a line laser as both heat excitation for active thermography and spatial coding for 3-D reconstruction. Furthermore, the model enables the feature-free registration of thermogram sequence and 3-D shape, so that the registration is fast and robust without keypoint features. The experiments on standard height specimens, 3-D printing glass fiber composites, and carbon fiber intake tube have shown the error is calibrated within 0.25 mm in the range of 1 to 150 mm and evidenced the capability for subsurface defects detection. The ease and robustness of the proposed active 3-D thermography have a bright future for 3-D measurement, defects detection, and quality control in the production line. [ABSTRACT FROM AUTHOR]

Microwave thermography (MWT) is a non-destructive testing method based on the principle of microwave heating, which is widely used in the detection of debonding defects of various composite materials. Based on the principle of Microwave thermography (MWT), a resonant cavity microwave thermography detection system is proposed in this paper. The temperature variation characteristics of debonding defects in carbon Fiber Reinforced Polymer (CFRP) and radar absorbing material (RAM) are studied. This system consists of a resonator microwave source, an infrared thermal imager, a fan and two computers. This system successfully detects such defects at the pixel level by thermally loading the test piece, collecting the temperature change data of the test piece by the thermal imager, analyzing the temperature difference between the normal area and the defect area. In this paper, the advantages of microwave on the radar absorbing material (RAM) with large specific heat capacity and high thermal conductivity, high heating efficiency, large heating area and negligible damage to the radar absorbing material are verified, and the high performance of the detection system is verified. The experimental results show that microwave thermography can realize high-precision debonding detection and positioning of multilayer heterogeneous radar absorbing material (RAM). [ABSTRACT FROM AUTHOR]

The future advancement of technology in Internet of Things (IoT) paradigm, Wireless Sensor Networks (WSNs) provide sensing services to connect all the devices. In the upper layer of OSI model designing an energy efficient routing protocol in WSN is a challenge, which can ease the work of Multi-access edge computing (MEC) in IoT applications. The advent of 6G is also playing key role for reliable communication between the sensing elements for IoT applications. These two phenomena are significantly influencing for the progress of next generation Intelligent Transportation System (ITS). Therefore, the proposed work presents a novel method of implementing Distributed Artificial Intelligence (DAI) with neural networks for energy efficient routing as well as a fast response for intra-cluster communication of the nodes to overcome the challenges for ITS. Although there exist several works on the inter-cluster energy-efficient network, our work proposes a new way of implementing the hybrid approach of DAI and Self Organizing Map (SOM). The proposed approach proves to be a better solution in terms of overall energy consumption by the network, along with the computational challenges. Further, the work presents mathematical analysis, simulation results and comparison with the conventional techniques for justification. [ABSTRACT FROM AUTHOR]

To solve the problems of low efficiency and manual dependence of industrial motor winding testing, a joint scanning electromagnetic thermographic (JSET) method and a new quantitative evaluation algorithm are proposed to inspect defects automatically and assess detection capability. We establish a JSET-based defect inspection system including a joint scanning model and induction heating to simulate industrial assembly lines and acquire real-time thermograms. However, the acquired thermograms are misaligned in time and space, which cannot be used for dimension analysis. Therefore, a new 3-D data reconstruction algorithm is proposed to achieve accurate spatial-temporal alignment of the image sequence. Moreover, the parameters (scanning speed and excitation current) of the developed inspection system are optimized through obtaining the maximum inspection quantity. The new quantitative evaluation algorithm can measure the detection capability of different defects types, sizes, and positions by two features of significance and detected area. Experimental results show that the proposed methods can inspect multiple motor winding defects automatically and enhance the inspect efficiency. [ABSTRACT FROM AUTHOR]

Joint scanning laser thermography (JLST) is well-known for its efficiency to overcome the field of view (FOV) limitation of thermal imagers. However, JSLT requires a data reconstruction to reveal the location of the defective area straightforwardly. Moreover, its detection capacity is limited by the lack of a deconvolution algorithm adaptive to the reconstructed data. In this study, a deconvolutional reconstruction method based on the Lucy–Richardson (LR) algorithm has been developed for JST, which is effective in suppressing random noise and the blur effect caused by the thermal diffusion. A JSLT inspection is carried out on a functional coating material with cylinder-like defects to test the performance of the proposed method. In comparison to the directly processed method on the original data, the proposed method is processed on the reconstructed data and then compared with principal component analysis (PCA), restored pseudo heat flux (RPHF), fast Fourier transform (FFT) methods and non-negative matrix factorization (NMF). The experimental results indicated that our proposed LR method exhibited a higher signal-to-noise ratio. Besides, it can detect the cylinder-mocked debonding defects with a diameter of 1.5 mm and a depth of 2.0 mm buried under the 1.0-mm coating. In addition, the defect detection diameter-to-depth ratio reached 1.5, while the defect detection rate of the test specimens can approach 90%. [ABSTRACT FROM AUTHOR]

Line scanning thermography (LST) is a fast non-destructive inspection (NDI) method for large-scale components. However, the current reconstruction algorithms for LST require the match between the frame rate and the scanning velocity. Moreover, these algorithms bring in spatial distortions. This paper proposes a non-orthogonal reconstructed space for LST. In such a space, the excitation motion in the line scanning process is described as a function of time delay, the moving velocity is estimated by the derived position map, and the reconstruction is achieved by image registration. The LST data are registered spatially by allowing the temporal misalignment among the pixels within the same reconstructed frame. The effectiveness of the proposed reconstruction algorithm is validated by both numerical simulations and experiments. The reconstruction results of experimental data indicate that the velocity is finely estimated so that the temporal alignment error is controlled within one pixel. Besides, no spatial distortions occur no matter how the frame rate and scanning velocity change. Furthermore, the Fourier phase image of the reconstructed LST reaches the diameter-to-depth ratio of 1.25 when inspecting a planar specimen with flat-bottom holes. [ABSTRACT FROM AUTHOR]

In the process of research, development, production, service, and maintenance of silicon photovoltaic (Si-PV) cells and the requirements for detection technology are becoming more and more important. This paper aims to investigate electromagnetic induction (EMI) and image fusion to improve the detection effect of electrothermography (ET) and electroluminescence (EL) of multidefects in Si-PV cells. First, the principles of ET, EL, and other physical processes including EMI, thermal radiation, and luminescence radiation are analyzed in this paper. ET and EL techniques after EMI improvement are used to detect different defects including scratch, broken gridline, surface impurity, hidden crack, and so on. The qualitative results show that EMI can greatly improve the defect detection ability of ET and EL. Then, an image-fusion rule based on L1 norm is proposed to fuse the sparse vector of the ET and EL images. The integration and complementarity of the two wavelength detection data are achieved. Finally, the image-fusion results of sparse representation (SR) algorithm is compared with discrete wavelet transform, curvelet transform, dual-tree complex wavelet transforms, and nonsubsampled contourlet transform. Five objective evaluation indexes including root mean square error, peak signal-to-noise ratio, correlation coefficient, mutual information, and structural similarity index are used to evaluate the fusion results. Overall evaluation results show that the SR algorithm is superior to the other algorithms. [ABSTRACT FROM AUTHOR]

In this paper, a new inspection method, joint scanning laser thermography (JSLT) as well as its data reconstruction and processing algorithm, is proposed. The new inspection method is utilized to detect and characterize the flat-bottom holes (FBH) in carbon fiber composites by using joint laser scanning scheme. By analyzing the nature of the thermal image sequences sampled under such a scanning scheme, a quick and simple reconstruction method is developed to characterize the buried depth of defects based on 1D heat conduction model. The processed thermal images are expected to get higher temporal resolution and spatial resolution. It can inspect larger area within shorter acquisition time than the pulse thermography. Thus, the study solves the dilemma between the inspection speed and the inspection capacity. Later, a joint laser scanning thermography test is set up to test the algorithm on a carbon fiber composite panel with defects buried at different depth. The experimental results show that the reconstructed data almost behave as those under pulse excitation. The tendency of temperature to change in the logarithmic domain over time is similar to the curve in the TSR method. But, unlike the pulse thermography data, the defect detection rate of PCA based on reconstructed data is higher than that of fast Fourier transform (FFT) amplitude image, independent component analysis (ICA) and FFT phase image. The JSLT system is used to detect FBHs and the diameter-depth ratio reached 3.33. [ABSTRACT FROM AUTHOR]