1,721,090 research outputs found
Thermography data fusion and non-negative matrix factorization for the evaluation of cultural heritage objects and buildings
No full textThe 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
Solar loading thermography: Time-lapsed thermographic survey and advanced thermographic signal processing for the inspection of civil engineering and cultural heritage structures
No full textThe experimental results from infrared thermography surveys over two buildings externally exposed walls are presented. Data acquisition was performed on a static configuration by recording direct and indirect solar loading during several days and was processed using advanced signal processing techniques in order to increase signal-to-noise ratio and signature contrast of the elements of interest. It is demonstrated that it is possible to detect the thermal signature of large internal structures as well as surface features under such thermographic scenarios. Results from a long-wave microbolometer compared favorably to those from a mid-wave cooled infrared camera for the detection of large subsurface features from unprocessed images. In both cases, however, advanced signal processing greatly improved contrast of the internal features
Improving the detection of thermal bridges in buildings via on-site infrared thermography: The potentialities of innovative mathematical tools
No full textThe detection of thermal bridges in buildings is one of the key points to be taken into account in energy saving procedures during refurbishment works. Passive infrared thermography (IRT) has been applied for years to detect thermal bridges by referring to the International Organization for Standardization (ISO) 6781:1983. However, the successfulness of this norm is strongly affected by the detection accuracy of the thermal imprint produced on the facade by a conductive material called as “defect” in this work. The drop shadow effect, also produced by the surrounding environment on the facade under inspection, plays indeed an important role during the defect evaluation procedure since it can mask/modify the natural thermal evolution due to diffusion. Many real-life signals acting in the space physics domain exhibit variations across different temporal scales. This work presents an application of a new multiscale data analysis method, the Iterative Filtering (IF), which allows to describe the multiscale nature of an electromagnetic signal working in the long-wave infrared (LWIR) region. IF appears to be a promising method minimizing the influence of the shadows projected on the facade under inspection; subsequently, it allows the optimization of the detection of thermal bridges via sparse principal component thermography (SPCT) technique. The latter inherits the advantages of PCT allowing more flexibility by introducing a penalization term. Here is shown how the accuracy of the defect detection increases after the application of the IF mathematical procedure. Results are discussed on the basis of a couple of case studies referring to dissimilar buildings. Finally, a signal-to-noise-ratio (SNR) comparison with raw data is added to the discussion of the results
Monitoring of jute/hemp fiber hybrid laminates by nondestructive testing techniques
No full textDamage following static indentation of jute/hemp (50 wt.% total fiber content) hybrid laminates was detected by a number of nondestructive testing (NDT) techniques, in particular, near (NIR) and short-wave (SWIR) infrared reflectography and transmittography, infrared thermography (IRT), digital speckle photography (DSP), and holographic interferometry (HI), to discover and evaluate real defects in a laminate with a complex structure. A comparative study between thermographic data acquired in the mid- (MWIR) and long-wave infrared (LWIR) spectrum bands, by pulsed (PT) and square pulse (SPT) thermography, is reported and analyzed. A thermal simulation by COMSOL® Multiphysics (COMSOL Inc., Burlington, MA, USA) to validate the heating provided is also added. The robust SOBI (SOBI-RO) algorithm, available into the ICALAB Toolbox (BSI RIKEN ABSP Lab, Hirosawa, Japan) and operating in the MATLAB® (The MathWorks, Inc., Natick, MA, USA) environment, was applied on SPT data with results comparable to the ones acquired by several thermographic techniques. Finally, segmentation operators were applied both to the NIR/SWIR transmittography images and to a characteristic principal component thermography (PCT) image (EOFs) to visualize damage in the area surrounding indentation
Enhancing defect detection in active infrared thermography using adaptive background suppression techniques
No full textRecent advancements in dimensionality reduction techniques have significantly contributed to the field of active infrared thermography (AIRT) for defect detection, aiding in data processing and feature extraction. Among these techniques, principal component thermography (PCT) and deep autoencoder thermography (DAT) are particularly notable. PCT is based on conventional linear multivariate analysis, while DAT leverages deep learning paradigms to better handle nonlinearity. These methods consolidate defect information from multiple thermograms into a concise set of feature images, enhancing the visibility of subsurface material defects. However, these feature images often suffer from disturbances, particularly non-uniform backgrounds caused by uneven heating in AIRT experiments. Such interferences can obscure defect information, necessitating further post-processing. In our research, we explore the efficacy of Adaptive Iteratively Reweighted Penalized Least Squares (AIR-PELS) as a refinement technique for PCT and DAT, focusing on background suppression. The adaptive iterative weighting with PELS smoothing effectively reduces noise and removes background disturbances. Case studies involving carbon fiber-reinforced polymer samples with inherent defects demonstrate the effectiveness of this post-processing approach
Multiscale Analysis of Solar Loading Thermographic Signals for Wall Structure Inspection
Full text linkInfrared thermography has been widely adopted in many applications for material structure inspection, where data analysis methods are often implemented to elaborate raw thermal data and to characterize material structural properties. Herein, a multiscale thermographic data analysis framework is proposed and applied to building structure inspection. In detail, thermograms are first collected by conducting solar loading thermography, which are then decomposed into several intrinsic mode functions under different spatial scales by multidimensional ensemble empirical mode decomposition. At each scale, principal component analysis (PCA) is implemented for feature extraction. By visualizing the loading vectors of PCA, the important building structures are highlighted. Compared with principal component thermography that applies PCA directly to raw thermal data, the proposed multiscale analysis method is able to zoom in on different types of structural features
Experimental and numerical research of debonding defects detection in fiber metal laminates using low-power ultrasonic-induced thermography
No full textDebonding defects in fiber metal laminates (FMLs) pose a significant threat to structural reliability, necessitating efficient and non-destructive inspection methods. This study explores the use of low-power ultrasonic-induced thermography (LUIT) for rapid visualization of debonding defects in FMLs through combined experimental and numerical investigations. An inspection system was developed, incorporating bispectral analysis for the determination of optimized excitation frequencies, thereby enhancing the heat generated at defect locations to achieve improved detection performance. Infrared thermography was employed to monitor transient temperature evolution, and a contrast-based time-slice selection strategy was introduced to enhance defect visibility. Furthermore, a comprehensive numerical simulation framework integrating modal analysis, implicit dynamic simulation, and thermo-mechanical coupling was proposed to reveal the underlying heating mechanisms, focusing on frictional dissipation, viscoelastic damping, and plastic deformation. The combined results demonstrate the capability of LUIT to selectively heat debonding defects without damaging the material, with defect detectability strongly influenced by defect size, depth, and excitation timing. The findings demonstrate that LUIT offers a fast, safe, and non-destructive approach for reliable debonding defect detection in FML structures
Reliability assessment of pulsed thermography and ultrasonic testing for impact damage of CFRP panels
No full textIn 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. a 90/95 values than ultrasonic testing for the parameters and setup used in the inspections of these thirty-five CFRP composite panels
A comparative study of enhanced infrared image processing for foreign object detection in lightweight composite honeycomb structures
No full textThe interest toward lightweight composites in the aeronautical industry grows year by year. The challenge is the identification and characterization of defects by using an integration of different techniques. The use of the infrared thermography (IRT) method for the inspection of lightweight composites is poorly documented in the open literature, due to the low heat diffusion through the honeycomb cores. In this study, IRT was used to retrieve the unknown positions of internal inserted objects in three different lightweight composite structures, by using three new thermal image processing integration methods. The corresponding post-processed results were compared against the traditional principal component thermography and pulsed phase thermography image processing techniques. The comparison showed that the integrated signal smoothing processing enhanced the image quality compared to the established techniques. Then, a thermal-physical analysis corresponding to the experimental results was conducted, in order to explain the experimental results. Finally, the advantages and disadvantages of the different presented methods when applied on the different lightweight composite structures were summarized.Peer reviewed: YesNRC publication: Ye
Solar loading thermography for architectural heritage surveys: plumb the depth by looking at the façade
No full textThe paper is centered on two case-studies in which the role of the sun as thermal stimulus is discussed working with the infrared thermography (IRT) technique. The first case-study is based on Santa Maria di Collemaggio Church in L’Aquila (Italy), initially built in the second half of the XIII century. The second case-study talks about a masterpiece of Venetian Gothic, i.e., Palazzo Ducale in Venice (Italy). In both cases, the striking jewel-box effect of the facades is due to a pattern of blocks of alternating pink and white stones. The facades and one lateral side of the Church constructed by using the masonry local system named, “apparecchio aquilano” have been inspected. Subsurface anomalies such as cracks, buried structures, humidity and metal reinforcements can be visualized exploiting the solar cycl
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