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Defects in photovoltaic panels can reduce the effective working area, and decrease the performance of the photovoltaic panels. Therefore, it is significant to identify such defects and to feed the fault information up to the production chain. Active infrared thermographic non-destructive testing has the advantages of non-contact, fast speed and large imaging area, and has been widely used in the detection of defects in various materials. In this article, a stepped thermography of the defects, which result in degradation of energy conversion efficiency of cells in photovoltaic panels, was proposed. The front surface of the photovoltaic panel was optically stimulated by halogen lamps in step heating way, while an infrared camera was employed to monitor the temperature evolution of the front surface and to capture infrared image sequences. The thermal image sequences were processed using data processing algorithms, focusing on enhancing the defect signatures. The techniques of background subtraction, discrete Fourier transform, polynomial fitting, first-order derivative, cross correlation coefficient and histogram equalization were mainly used. Compared to the raw thermal images, the reconstructed images using suitable algorithms are more helpful to evaluate the defects. The results show that the optical stepped thermography combined with suitable post data processing is a fast and effective technique to identify the defects in photovoltaic panels.

Ultrasonic infrared thermography is a rapid and informative method of nondestructive testing, used to inspect materials and products in aviation and rocket and space industries. High-power piezoelectric emitters stimulate various materials by ultrasound and help revealing hidden flaws by local temperature changes, which can amount to tens of degrees. The possibility of optimizing the procedure of ultrasonic infrared thermography by choosing the frequency of acoustic waves so as to match the frequencies of local resonances of defects of different sizes has been investigated.

The quality control of composite materials remains an important topic of research in nondestructive testing (NDT) of modern materials, in particular, the ones intended for aerospace industry. Over the last decade, there has been a noticeable increase in interest to the ultrasonic stimulation of tested materials and analysis of dynamic temperature distributions. This method is commonly implemented with two techniques, viz. high-power stimulation at a fixed acoustic-signal frequency and low-power resonant ultrasonic stimulation. Results are provided on comparing the above ultrasonic NDT methods using the example of an impact damage in a graphite epoxy-filled composite. The comparison is made based on the criteria of temperature signals, revealed flawed zone, and productivity of testing.

Solid rocket motors (SRMs) have been widely applied in missiles, space rockets and shuttles. However, because of some technological and environmental reasons, debonding or poor bonding usually appears in the insulator of SRMs. To evaluate the bonding state between the insulator and the shell during manufacturing, a phase sensitive modulated thermography (MT) is proposed. In order to optimize the operating condition, the influences of the modulation frequency, insulator thickness and debonding lateral size on defect signals are studied through experimentation and 3D numerical simulation. Finite element analysis (FEA) using ANSYS software is applied to simulate the thermal process of the structures precisely. The results show that phase sensitive MT is an effective approach to detect the debondings in the adhesion interfaces. The phase difference at the modulation frequency can be expressed as a quintic polynomial of the modulation frequency in the usual operating frequency range, and the best modulation frequency and detectable frequency band can be determined by the polynomial; when the insulator thickness increases, the best modulation frequency and the maximum phase difference decrease; when the debonding lateral size increases, the maximum phase difference increases while the best modulation frequency remains unchanged. The results provide a guideline for the MT of debondings in SRMs.

Pulsed thermography has been widely used in the inspection of composite materials. The detection limit of pulsed thermography of the materials is often a question being concerned by end users. However, the detection limit cannot be described only by a simple parameter, and is affected by many factors associated with the defect, material, excitation, infrared camera and data processing algorithm. The current concept of detection limit defined with the minimum detectable size, the maximum detectable depth or the minimum aspect ratio depends on the specific experimental conditions, and it lacks universality when the extrinsic experimental conditions change. The aim of this paper is to establish a unified standard model of the detection limit which can be applied to various experimental conditions. The solution was to decouple the material’s intrinsic thermal property and extrinsic experimental condition dependencies and then allow them to be described independently. A new expression of the detection limit of the PT was firstly proposed by creating a differential temperature contour map. Taking delaminations in a C/SiC composite as detecting defects, an experiment was conducted to assess the feasibility and the detection limit, and to evaluate the noise level in practice. The influences of defect size and defect depth on the defect informative parameters were analyzed by using the finite element method based on 2D modeling. Eventually, the differential temperature contour map was calculated for the pulsed thermography of delaminations of C/SiC composites. The results show that the differential temperature contour map proposed can strictly describe the detection limit due to its differential temperature invariance property, and it provides a comprehensive tool to consider many experimental parameters including thermal excitation energy intensity, noise level, and SNR threshold by using a criterion equation in practice.

Ultrasonic IR thermography is a powerful tool for the inspection of cracks, but the quality of raw image sequence varies greatly due to many factors. In order to facilitate an automatic identification of low contrast defects in ultrasonic thermography, a defect identification method was proposed based on parameter estimation of gray-level histogram. Taking an aluminum alloy beam as a sample, the crack identification was demonstrated. After selecting the suitable data from the raw image sequence, the mean value and standard deviation of the histogram for a kind of excess thermograms were estimated, and then the image enhancement and defect segmentation were performed according to the statistical parameters. An unsupervised process flow was concluded together with adaptive operation parameters. The results show that the method is fast, effective, robust and adaptive for various qualities of experimental data acquired in different test conditions. The method can improve the detectability of ultrasonic thermography and facilitate the automatic identification of defects.

Ultrasound-excited vibrothermography (VT) is a newly developed version of InfraRed (IR) thermographic Non-Destructive Testing (NDT), but the relation between the defect signal and mechanical parameters is fuzzy due to the complexity of thermo-mechanical coupling and the interaction effects of multiple factors, so the design of operation conditions of VT is highly-dependent on the experience of operators. In order to reveal the relations of the defect signal vs. the mechanical parameters, and to enable the optimisation of operation conditions, a mathematical model was presented to simulate the heat generation and heat transfer that occur during VT. The heat power produced in a crack and the temperature increase at the crack area were solved by an analytical approach. The function of the temperature increase at the crack area vs. the mechanical parameters including the damp, mass, stiffness, amplitude and frequency of the stimulation force, friction coefficient and normal force in crack faces, excitation du...

Ultrasound-stimulated IR thermography, thanks to its large-area imaging capability, high test productivity and safety, is a powerful tool for the inspection of cracks in heavy aluminum structures. In thick aluminum parts, the most important defect detection parameters are the differential temperature signal and signal-to-noise ratio (SNR), which typically reach their maximums at shortly (under 1 s) after the beginning of the ultrasonic excitation. In the IR inspection of non-metals, the ultrasonic excitation may be relatively long, while in the case of highly-conductive aluminum, short-pulse (burst) stimulation (from 0.4 to 1 s) is sufficient The crack detectability can be improved by evaluating temperature images at the times when maximum SNR values occur. Further enhancement of test results can be achieved by applying some data processing algorithms which can be 1D, i.e. applied to temperature evolutions in time, or 2D, i.e. applied to spatial coordinates, or a single image.

Qilian Mountain permafrost,with an area of about 10×104km2,is located in the north of Qinghai-Tibet plateau. It has of perfect conditions and great prospecting potential for gas hydrate. The Scientific Drilling Project of Gas Hydrate in the Qilian Mountain permafrost,which locates at the Jugenghu Mining Area in the Muli Coalfield,Tianjun County,Qinghai Province,has been implemented by China Geological Survey in 2008~2009. Four scientific drilling wells have been completed with a total footage of 2059.13m. Samples of gas hydrate were collected separately in the holes of DK-1,DK-2 and DK-3. Gas hydrate is hosted under permafrost zone at the 133~396m interval. The samples are white or ivory crystal and easily burning. Distinct low temperature anomaly has been identified by infrared camera. The gas hydrate-bearing cores strongly bubble in water. Gas-bubble and water-drop emit from the samples and then retain characteristic honeycombed structure. The typical spectrum curve of gas hydrate is detected using Raman spectrometry. Furthermore,the logging profile also indicates high electrical resistivity and sonic velocity. Gas hydrate in Qilian Mountain is found within a thinner permafrost zone with a shallower buried depth and characterized by more complex gas component and coalbed methane origin,suggesting a new type of hydrate.