Defect Detection in Weld Joints by Infrared Thermography

The objective of this present paper is to evaluate the effectiveness of infrared thermography (IRT) as a non-contact, fast and reliable non-destructive evaluation procedure for detection and quantification of defects in weld joints. In the present work, a friction stir welded (FSW) joint of two aluminum plates and three 316 LN stain-less steel (SS) weld-joints with lack of penetration (LOP), lack of fusion (LOF) and tungsten inclusion (TI) defects respectively, were inspected using IRT and digital radio-graphy (DRG). Using active thermography methods, a sub-surface tunnel defect along the weld line was successfully detected in the FSW joint and its length and width were estimated by suitable pixel calibration. Using lock-in thermography, optimum frequencies were determined for each of the specimens and defect-depths were estimated. Tempe-rature fall of the defect region and defect-free region were monitored as a function of time and it was found that the rate of temperature fall in the former case is slower than that in the latter one. Results from both the tech-niques, i.e., IRT and DRG were found to be in good agree-ment with each other in all the cases. Advantage of IRT is that it provides depth information also

Development of high-resolution infrared thermographic imaging method as a diagnostic tool for acute undifferentiated limp in young children

Acute limp is a common presenting condition in the paediatric emergency department. There are a number of causes of acute limp that include traumatic injury, infection and malignancy. These causes in young children are not easily distinguished. In this pilot study, an infrared thermographic imaging technique to diagnose acute undifferentiated limp in young children was developed. Following required ethics approval, 30 children (mean age = 5.2 years, standard deviation = 3.3 years) were recruited. The exposed lower limbs of participants were imaged using a high-resolution thermal camera. Using predefined regions of interest (ROI), any skin surface temperature difference between the healthy and affected legs was statistically analysed, with the aim of identifying limp. In all examined ROIs, the median skin surface temperature for the affected limb was higher than that of the healthy limb. The small sample size recruited for each group, however, meant that the statistical tests of significant difference need to be interpreted in this context. Thermal imaging showed potential in helping with the diagnosis of acute limp in children. Repeating a similar study with a larger sample size will be beneficial to establish reproducibility of the results

Cutting tool tracking and recognition based on infrared and visual imaging systems using principal component analysis (PCA) and discrete wavelet transform (DWT) combined with neural networks

The implementation of computerised condition monitoring systems for the detection cutting tools’ correct installation and fault diagnosis is of a high importance in modern manufacturing industries. The primary function of a condition monitoring system is to check the existence of the tool before starting any machining process and ensure its health during operation. The aim of this study is to assess the detection of the existence of the tool in the spindle and its health (i.e. normal or broken) using infrared and vision systems as a non-contact methodology. The application of Principal Component Analysis (PCA) and Discrete Wavelet Transform (DWT) combined with neural networks are investigated using both types of data in order to establish an effective and reliable novel software program for tool tracking and health recognition. Infrared and visual cameras are used to locate and track the cutting tool during the machining process using a suitable analysis and image processing algorithms. The capabilities of PCA and Discrete Wavelet Transform (DWT) combined with neural networks are investigated in recognising the tool’s condition by comparing the characteristics of the tool to those of known conditions in the training set. The experimental results have shown high performance when using the infrared data in comparison to visual images for the selected image and signal processing algorithms

A novel infrared video surveillance system using deep learning based techniques

This is the author accepted manuscript. The final version is available from Springer via the DOI in this record.This paper presents a new, practical infrared video based surveillance system, consisting of a resolution-enhanced, automatic target detection/recognition (ATD/R) system that is widely applicable in civilian and military applications. To deal with the issue of small numbers of pixel on target in the developed ATD/R system, as are encountered in long range imagery, a super-resolution method is employed to increase target signature resolution and optimise the baseline quality of inputs for object recognition. To tackle the challenge of detecting extremely low-resolution targets, we train a sophisticated and powerful convolutional neural network (CNN) based faster-RCNN using long wave infrared imagery datasets that were prepared and marked in-house. The system was tested under different weather conditions, using two datasets featuring target types comprising pedestrians and 6 different types of ground vehicles. The developed ATD/R system can detect extremely low-resolution targets with superior performance by effectively addressing the low small number of pixels on target, encountered in long range applications. A comparison with traditional methods confirms this superiority both qualitatively and quantitativelyThis work was funded by Thales UK, the Centre of Excellence for Sensor and Imaging System (CENSIS), and the Scottish Funding Council under the project “AALART. Thales-Challenge Low-pixel Automatic Target Detection and Recognition (ATD/ATR)”, ref. CAF-0036. Thanks are also given to the Digital Health and Care Institute (DHI, project Smartcough-MacMasters), which partially supported Mr. Monge-Alvarez’s contribution, and to the Royal Society of Edinburgh and National Science Foundation of China for the funding associated to the project “Flood Detection and Monitoring using Hyperspectral Remote Sensing from Unmanned Aerial Vehicles”, which partially covered Dr. Casaseca-de-la-Higuera’s, Dr. Luo’s, and Prof. Wang’s contribution. Dr. Casaseca-de-la-Higuera would also like to acknowledge the Royal Society of Edinburgh for the funding associated to project “HIVE”

Characterisation of adhesively bonded laminates using radiography and infrared thermal imaging techniques

This paper discusses the studies undertaken to establish the limits of detectability of glue variations and defect detection in adhesively bonded canopy specimens used in fighter aircrafts, by low-energy radiography and thermal imaging techniques. The complementary nature of radiography and thermal imaging is also highlighted. From the results of the experiments, it is shown that low-energy radiography reveals volumetric defects such as porosities as small as 170 microns; thermal imaging is more sensitive to evaluate variations in glue content, having the capability of detection of variations in glue content of the order of 20%. Thermal imaging did not indicate the presence of any major defects such as delaminations and debonds. While reflection and through-transmission-based thermal imaging techniques can be utilised for the detection of glue variations, the rate of change of temperature by transmission technique being higher, this can serve as a more sensitive indicator of glue variations, if both sides access is feasible

Enhanced sensitivity detection of defects in gas turbine blades of aero-engine and hairpin tubes of heavy water plant using microfocal radiography

This paper reports on the microfocal radiography (MR) procedure for detection of shrinkage defects and imaging of internal parts of the cooling channels of aero-engine gas turbine blades. MR, with inherent magnification, has been used for evaluation of blisters in hairpin tubes subjected to service conditions. The results are compared with the conventional radiography technique and it is found that MR can provide additional features, such as internals in turbine blades, shrinkage defects etc. In the case of hairpin tubes, it has been found that microfocal radiography is an ideal technique for detecting defects such as blisters and branching cracks with better sensitivity than conventional radiography. Results also show that MR is the best choice for quality control of aero-engine turbine blades at the production stage in order to have control over the process parameters and for failure analysis of hairpin tubes used in Heavy Water Plants (HWP)

Segmentation of defects from radiography images by the histogram concavity threshold method

A histogram concavity-based thresholding approach has been used for segmenting porosities, voids and inclusions from digitised radiography images. Studies on small defects in cylindrical tubes and flat plates show that histogram concavity-based thresholding approach yields better results compared to conventional automatic thresholding techniques like Maximum Entropy and Otsu methods, where heterogeneous background present in the image due to the geometry and large X-ray quantum and structural noise gives poor segmentation. The segmentation procedure is optimised by filtering the abrupt noises with low-pass median filtering, followed by grey-level global thresholding. The histogram concavity-based thresholds provided excellent segmentation of pores and voids present in Electrochemical Hydrogen Sensors (ECHS), cast plates used in refineries, micro-pores in tube-to-tubesheet (TTS) welds of steam generator of fast breeder reactor, tungsten inclusion in Tungsten Inert Gas (TIG) welds, crater crack and external undercut in welds. An accuracy of ± 1-2 pixel sizes (65-130 microns) is obtained for the estimation of defects sizes from the grey-level profiling