Showing papers on "Thermography published in 2019"

High-resolution remote thermometry and thermography using luminescent low-dimensional tin-halide perovskites.

Abstract: Although metal-halide perovskites have recently revolutionized research in optoelectronics through a unique combination of performance and synthetic simplicity, their low-dimensional counterparts can further expand the field with hitherto unknown and practically useful optical functionalities. In this context, we present the strong temperature dependence of the photoluminescence lifetime of low-dimensional, perovskite-like tin-halides and apply this property to thermal imaging. The photoluminescence lifetimes are governed by the heat-assisted de-trapping of self-trapped excitons, and their values can be varied over several orders of magnitude by adjusting the temperature (up to 20 ns °C−1). Typically, this sensitive range spans up to 100 °C, and it is both compound-specific and shown to be compositionally and structurally tunable from −100 to 110 °C going from [C(NH2)3]2SnBr4 to Cs4SnBr6 and (C4N2H14I)4SnI6. Finally, through the implementation of cost-effective hardware for fluorescence lifetime imaging, based on time-of-flight technology, these thermoluminophores have been used to record thermographic videos with high spatial and thermal resolution. Low-dimensional tin-halide perovskites exhibit strong temperature dependence of luminescence decay time that translates into high sensitivity over a wide range of temperatures and as such can be used in high-resolution remote thermography.

Deep learning–based autonomous concrete crack evaluation through hybrid image scanning:

Abstract: This article proposes a deep learning–based autonomous concrete crack detection technique using hybrid images. The hybrid images combining vision and infrared thermography images are able to improv...

Temporal and spatial deep learning network for infrared thermal defect detection

Abstract: Most common types of defects for composite are debond and delamination. It is difficult to detect the inner defects on a complex shaped specimen by using conventional optical thermography nondestructive testing (NDT) methods. In this paper, a hybrid of spatial and temporal deep learning architecture for automatic thermography defects detection is proposed. The integration of cross network learning strategy has the capability to significantly minimize the uneven illumination and enhance the detection rate. The probability of detection (POD) has been derived to measure the detection results and this is coupled with comparison studies to verify the efficacy of the proposed method. The results show that visual geometry group-Unet (VGG-Unet) cross learning structure can significantly improve the contrast between the defective and non-defective regions. In addition, investigation of different feature extraction methods in which embedded in deep learning is conducted to optimize the learning structure. To investigate the efficacy and robustness of the proposed method, experimental studies have been carried out for inner debond defects on both regular and irregular shaped carbon fiber reinforced polymer (CFRP) specimens.

New features for delamination depth evaluation in carbon fiber reinforced plastic materials using eddy current pulse-compression thermography

Abstract: The growing application of composite materials in aerospace leads to the urgent need of non-destructive testing and evaluation (NDT&E) techniques capable of detecting defects such as impact damage and delamination possibly existing in those materials. Eddy current pulsed thermography is an emerging non-destructive testing (NDT) technique capable of detecting such defects. However, characterization of delamination within composite materials is difficult to be achieved by a single pulse excitation, especially in carbon fiber reinforced plastic materials as the extraction of thermal diffusion in such multi-layered structures is challenging. To cope with this problem of signal-to-noise ratio, this paper proposes the eddy current pulse-compression thermography (ECPuCT), combining the Barker code modulated eddy current excitation and pulse-compression technique to enhance the capability of characterizing delamination on carbon fiber reinforced plastic materials. Additionally, a thermal pattern enhanced method based on kernel principal component analysis technique is used to locate the delaminated areas. Two features, including a newly proposed crossing point of impulse responses related to defective and non-defective areas and skewness of impulse responses are investigated for delamination depth evaluation. Results show that delamination can be detected within depths ranging from 0.46 mm to 2.30 mm and both the proposed features have a monotonic relationship with delamination depths.

Thermal Imaging and Vibration-Based Multisensor Fault Detection for Rotating Machinery

Abstract: In order to minimize operation and maintenance costs and extend the lifetime of rotating machinery, damaging conditions and faults should be detected early and automatically. To enable this, sensor streams should continuously be monitored, processed, and interpreted. In recent years, infrared thermal imaging has gained attention for the said purpose. However, the detection capabilities of a system that uses infrared thermal imaging is limited by the modality captured by this single sensor, as is any single sensor-based system. Hence, within this paper a multisensor system is proposed that not only uses infrared thermal imaging data, but also vibration measurements for automatic condition and fault detection in rotating machinery. It is shown that by combining these two types of sensor data, several conditions/faults and combinations can be detected more accurately than when considering the sensor streams individually.

A new model for fatigue life prediction based on infrared thermography and degradation process for CFRP composite laminates

Abstract: In this paper, a new fatigue life prediction methodology is proposed by combining stiffness degradation and temperature variation measured by InfraRed Thermographic (IRT) camera. Firstly, the improved thermographic method is used to determine the fatigue limit by using the data of stabilized temperature rising. Following this, a two-parameter model is proposed to characterize the stiffness degradation of CFRP laminates with the increase of cycle numbers. After the calibration parameters and the calculation of the normalized failure threshold stiffness, the whole S - N curve can be obtained in a very short time. The proposed model is applied to both the experimental data of triaxially braided CFRP laminates from literature and those of unidirectional CFRP laminates obtained from our fatigue tests. Results show that predicted S - N curves have a good agreement with traditional tests. The principal interests of this model could be listed as follows: (i) it is a more general criterion applicable to different materials; (ii) it has more physical senses; (iii) it allows the determination of the total S-N curve for composite materials in a short time.

DC-Biased Magnetization Based Eddy Current Thermography for Subsurface Defect Detection

Abstract: Eddy current thermography (ECT) as one of the emerging nondestructive testing and evaluation techniques has been used for defects detection in critical components, e.g., fatigue cracks in turbine blades, bond wire lift-off in IGBT modules, lack of fusion in welded parts, etc. However, in fast inspection using the early thermal response, the thin eddy current penetration depth (skin depth) of ferromagnetic materials limits ECT's capability of detecting subsurface defects. In order to increase the detectable depth range, this paper proposes a dc-biased magnetization based ECT (DCMECT) technique. Based on the nonlinear magnetic permeability in ferromagnetic material, DCMECT can increase the thermal contrast between the defective and sound areas by the enhanced permeability distortion in the skin-depth layer. Specifically, the influences of dc-biased magnetization direction and intensity on the thermal responses (of the defective and sound areas) and their thermal contrast are investigated. Results show that the dc-biased magnetization direction has the strongest influence on the thermal response when it is parallel to the ac magnetization direction generated by the coil. Both the thermal responses of defective and sound areas decrease with the magnetization intensity increasing. Whereas, the thermal contrast between two areas increases with the magnetization intensity, which presents the enhanced defect detectability of DCMECT. The proposed technique can detect the subsurface defect with a buried depth up to 6 mm.

Reliability assessment of pulsed thermography and ultrasonic testing for impact damage of CFRP panels

Abstract: In order to quantitatively compare the reliability of pulsed thermography and ultrasonic testing techniques, a set of thirty-five Carbon Fiber Reinforced Plastic (CFRP) composite panels with impact damages are inspected by pulsed thermography and ultrasonic C-scan. The comparative experimental results and Probability of Detection (PoD) analysis results are presented. The quantitative comparison shows that pulsed thermography testing has smaller defect size at 90% PoD with 95% confidence level, i.e. a90/95 values than ultrasonic testing for the parameters and setup used in the inspections of these thirty-five CFRP composite panels.

Automated defect classification in infrared thermography based on a neural network

Abstract: This paper reports on the use of a neural network in infrared thermography to classify defects, such as air, oil, and water, which can degrade material performance. A finite element method and experiment were adopted to simulate air, water, and oil ingress. Raw data, and thermographic signal reconstruction coefficients were used to train, and test the two multilayer, feed-forward NN models. Quantitative comparisons showed that the model using coefficients as features performed better than the one using raw data. It was more precise and had better test repeatability. This indicates the model is more generalizable.

Breast Cancer Detection in Thermal Infrared Images Using Representation Learning and Texture Analysis Methods

Abstract: Nowadays, breast cancer is one of the most common cancers diagnosed in women. Mammography is the standard screening imaging technique for the early detection of breast cancer. However, thermal infrared images (thermographies) can be used to reveal lesions in dense breasts. In these images, the temperature of the regions that contain tumors is warmer than the normal tissue. To detect that difference in temperature between normal and cancerous regions, a dynamic thermography procedure uses thermal infrared cameras to generate infrared images at fixed time steps, obtaining a sequence of infrared images. In this paper, we propose a novel method to model the changes on temperatures in normal and abnormal breasts using a representation learning technique called learning-to-rank and texture analysis methods. The proposed method generates a compact representation for the infrared images of each sequence, which is then exploited to differentiate between normal and cancerous cases. Our method produced competitive (AUC = 0.989) results when compared to other studies in the literature.

Comparative analysis of eddy current pulsed thermography and long pulse thermography for damage detection in metals and composites

Abstract: Eddy current pulsed thermography (ECPT) and long pulse thermography (LPT) are two emerging non-destructive evaluation techniques that have been recently used for sub-surface defect detection in both metallic and composite components. This paper provides a comparative analysis of these two methods by focussing on the detection of flat bottom holes (FBHs) in both aluminium and carbon fibre reinforced plastic samples. Principal component analysis was used for the post-processing of thermal data in order to enhance the detection of FBHs with various diameters and depths. Results showed that LPT had a better performance for detecting FBHs in low-thermally conductive materials such as carbon fibre-reinforced thermoplastic composites, whilst ECPT revealed a superior performance for detecting FBHs in materials with higher thermal and electrical conductivity such as aluminium.

An investigation of the stepped thermography technique for defects evaluation in GFRP materials

Abstract: The ability of thermography to detect defects in composite materials has been demonstrated and showed in various works and in many applications. In this regard, various NDT techniques are currently used for defect detection in composites such as Lock-in Thermography (LT), Pulsed (PT) Stepped Thermography (ST/SH), all of which have their own peculiarities and capabilities. A critical aspect concerns the overall lengthy time required for testing and analysis of thermographic data above all, for large structure where it is necessary a scanning approach. In this work, two algorithms based on the stepped thermography approach were investigated in quantitative way with the aim to optimizing the testing parameters and data analysis in terms of testing time and signal to noise ratio. In particular, several tests were carried out on a sample specimen with simulated defects and the well-established lock-in thermography technique has been used as comparison.

Thermography data fusion and nonnegative matrix factorization for the evaluation of cultural heritage objects and buildings

Abstract: The application of the thermal and infrared technology in different areas of research is considerably increasing. These applications involve nondestructive testing, medical analysis (computer aid diagnosis/detection—CAD), and arts and archeology, among many others. In the arts and archeology field, infrared technology provides significant contributions in terms of finding defects of possible impaired regions. This has been done through a wide range of different thermographic experiments and infrared methods. The proposed approach here focuses on application of some known factor analysis methods such as standard nonnegative matrix factorization (NMF) optimized by gradient-descent-based multiplicative rules (SNMF1) and standard NMF optimized by nonnegative least squares active-set algorithm (SNMF2) and eigen-decomposition approaches such as principal component analysis (PCA) in thermography, and candid covariance-free incremental principal component analysis in thermography to obtain the thermal features. On the one hand, these methods are usually applied as preprocessing before clustering for the purpose of segmentation of possible defects. On the other hand, a wavelet-based data fusion combines the data of each method with PCA to increase the accuracy of the algorithm. The quantitative assessment of these approaches indicates considerable segmentation along with the reasonable computational complexity. It shows the promising performance and demonstrated a confirmation for the outlined properties. In particular, a polychromatic wooden statue, a fresco, a painting on canvas, and a building were analyzed using the above-mentioned methods, and the accuracy of defect (or targeted) region segmentation up to 71.98%, 57.10%, 49.27%, and 68.53% was obtained, respectively.

Active Microwave Thermography for Nondestructive Evaluation of Surface Cracks in Metal Structures

Abstract: Detection of covered surface cracks in metal structures is an important issue in numerous industries. Various nondestructive testing (NDT) and evaluation techniques have been applied for this goal with varying levels of success. Recently, a technique based on the integration of microwave and thermographic NDT, herein referred to as active microwave thermography (AMT), has been considered for various applications. In AMT, electromagnetic energy is utilized for the thermal excitation, and the subsequent surface thermal profile of the structure/material under test is measured with a thermal camera. Utilizing electromagnetic energy allows the inspection to be tailored to the application through choice of frequency, polarization, and power level. It is shown that for metal with a dielectric-filled crack irradiated with an electric field (E-field) polarized perpendicular to the crack length, a propagating mode (TE10) is generated inside the crack, which causes dielectric heating to occur in the (filled) crack. In the particular case of study in this paper, based on the excitation power and the thermal camera sensitivity, the crack can be detected via an AMT inspection as long as the angle between the crack length and incident E-field is between 0° (perpendicular polarization) and ~65°. In addition, from the measured thermal contrast and signal-to-noise ratio, the optimum heating time is ~5–30 s for successful detection.

Automatic detection of moistures in different construction materials from thermographic images

Abstract: Moisture is a pathology that damages all type of construction materials, from materials of building envelopes to materials of bridges. Its presence can negatively affect the users’ conditions of indoor comfort. Furthermore, heating and cooling energy demand can be increased by the presence of moist materials. Infrared thermography (IRT) is a common technique in the scientific field to detect moisture areas, because of its non-destructive, non-contact nature. In addition, IRT allows an earlier moisture detection compared to the analysis using visible images. In order to optimize thermographic inspections, this paper presents one of the first methodologies for the automatic detection of moisture areas affecting the surface of construction materials. The methodology is based on the application of visible image processing techniques adapted to thermographic images through the consideration of an image conversion format, a thermal criterion and a thermal and a geometric filter. The precision, recall and F-score parameters obtained are around 83.5%, 73.5% and 72.5%, respectively, considering the false positives/negatives through a series of 12 tests made in different construction materials and ambient conditions, comparing the preliminary results with existing methodologies.

Pulsed Phase Thermography Approach for the Characterization of Delaminations in CFRP and Comparison to Phased Array Ultrasonic Testing

Abstract: Pulsed phase thermography (PPT) is a well-established algorithm used for processing thermographic data in frequency domain with the aim to extract information about the defect size and depth. However, few works demonstrated the capability of PPT technique in defects evaluation in real components. The aim of this work is the assessment of capability of PPT technique in determining delaminations in CFRP components used in aeronautics. The component chosen for implementing the technique has a non-uniform geometry and the defects inside it are not simulated, but they are real and generated during the production process. The specimen has been investigated through the application of both the ultrasonic technique and the thermographic one. Thermographic phase images elaborated with a suitable computational processing have been compared with Ultrasonic C-scan images and, the agreement between the location and depth of defects has been verified. Besides, the ultrasonic technique has been used to validate the PPT results.

Estimation of Damage Thickness in Fiber-Reinforced Composites using Pulsed Thermography

Abstract: Nondestructive testing (NDT), including active thermography, has become an inevitable part of composite process and product verification, post manufacturing. However, there is no reliable NDT technique available to ensure the interlaminar bond integrity during composite laminate integration, bonding or repair where the presence of thin air gaps in the interface of dissimilar polymer composite materials would be detrimental to structural integrity. This paper introduces a novel approach attempting to quantify the damage thickness of composites (the thickness of air gaps inside composites) through a single-side inspection of pulsed thermography. The potential of this method is demonstrated by testing on three specimens with different types of defect, where the Pearson correlation coefficients of the thickness estimation for block defects and flat-bottom holes are 0.75 and 0.85, respectively. This approach will considerably enhance the degradation assessment performance of active thermography by extending damage measurement from currently two dimensions to three dimensions, and become an enabling technology on quality assurance of structural integrity.

Barker-Coded Thermal Wave Imaging for Non-Destructive Testing and Evaluation of Steel Material

Abstract: Thermal non-destructive testing (TNDT) is one of the emerging inspection and evaluation techniques mostly used for subsurface defect detection in various industrial components. Besides the conventional thermography techniques (such as lock-in and pulse), recently introduced non-stationary thermal wave imaging (NSTWI) techniques gained its applicability in TNDT community due to their inherent testing capabilities such as improved sensitivity and enhanced resolution in inspecting and evaluating various solid materials for detecting subsurface defects. Barker-coded thermal wave imaging (BCTWI) is a one of the widely used NSTWI techniques, which facilitates the use of low peak power heat sources in moderate experimentation time in contrast to conventional TNDT techniques. In this paper, the pulse compression favorable NSTWI (BCTWI), the reconstructed pulsed (main lobe) data have been considered and processed using independent component analysis and named Barker-coded independent component thermography (BCICT). This proposed BCICT is implemented on a mild-steel sample to detect the artificially simulated flat bottom circular holes located at different depths inside it. The proposed technique extracts the sub-surface details such as flaws/defects hidden inside the sample by an unsupervised learning process, which helps in eliminating the manual interpretation of subsurface defects. The applicability of the proposed algorithm has been evaluated and validated experimentally with two different excitations schemes by considering the contrast and signal-to-noise ratio (SNR) as figure of merit. The results indicate that the BCICT technique offers higher contrast and SNR in comparison to conventional pulse-based TNDT technique.