Infrared Thermography Inspection Research Articles

Infrared Thermography Inspection of Stay Cables

The injection of petroleum waxes into bridge stay cables is a proven method for protection against corrosion, primarily due to their sealing properties, adhesion to strands, and thermal stability. However, with a liquefaction point below 70°C, exceptional ambient conditions can lead to partial liquefaction of the injected waxes, especially for cables with a dark sheath, resulting in leaks and extrusions. Sections of stay cables may lose their corrosion protection without visible signs from the outside. This study explores the use of infrared thermography for identifying and mapping areas lacking wax. It involves acquiring high-resolution infrared thermal images of the stay cables and studying temperature variations. Since wax has a higher thermal inertia than air, a thermal gradient is expected between areas with wax and those without after the daily heating cycle. This paper presents a campaign conducted on a cable-stayed bridge in France. During this campaign, temporary instrumentation was deployed to identify the optimal intervention window. Multiple sessions of acquisitions using a high-precision thermal camera were then performed. The acquired images were subsequently rectified and assembled to create uniform and comprehensive panoramas of the stay cables. The results obtained for several characteristic cables are presented and discussed in this paper.

A flexible deep learning framework for thermographic inspection of composites

Infrared thermography (IRT) is a promising inspection technique, showing good defect detectability in a range of materials. To advance the IRT inspection technique, automated defect detection and analysis are of high interest. This study proposes an object detection algorithm based on Faster R–CNN with attention-based feature fusion network and flexible late fusion strategy for efficient extraction of defect features from an IRT image sequence. In order to train the deep learning-based algorithm, a large, diverse and representative virtual thermographic dataset for composite materials was constructed by an in-house developed parameterized 3D finite element simulator. The virtually trained deep learning framework is tested on experimental IRT datasets which were obtained on composite coupons having a range of artificial defect types as well as on stiffened aerospace composite panels with real (production) defects. The obtained test results indicate the high performance, generalization and reliability of the proposed deep learning framework for automated thermographic inspection of composite parts.

Suppressing reflected radiation to enhance infrared thermography inspection

Infrared thermography is commonly used in a variety of applications. It is a fast, passive, non-contact, non-invasive alternative to conventional techniques. Nevertheless, its use has several associated errors that must be minimized or eliminated. One source of error is the radiation reflected on the surface of the studied object, coming from the surroundings, and captured by the thermographic camera lens. A measurement of the reflected apparent temperature may compensate for this error. In fact, during the thermographic inspections, the operator generally assumes that the reflected apparent temperature is equal to the ambient temperature, resulting in a less accurate thermal image pattern. This study aims to verify the impact of suppressing reflected radiation on improving the thermal image details of the thermograms obtained during thermographic inspections. For this purpose, a wood sample was observed in the presence and absence of heat and light sources. The results suggest that the suppression of reflected radiation leads to better-quality thermograms and enhances the accuracy of defect detection and identification.

Considerations Regarding Electrical Equipment Monitoring Through Infrared Thermography

Abstract In the paper are highlighted the main advantages of wireless monitoring methods present in the market for electrical equipment temperature surveillance. Also, are considerate the mandatory corrections applied to the thermographic image in order to adjust the electrical equipment temperature. In the final part, there are shown the resulted values of an infrared thermography inspection performed in industrial environment, which reveals the thermal stresses identification of the busbar’s connections from a medium voltage switch separator. In addition, the paper present why a highly reflexive surface is not suitable for infrared thermography, and in what manner affects the data obtained from the monitoring devices.

Development of a thermal excitation source used in an active thermographic UAV platform

ABSTRACT This work aims to address the effectiveness and challenges of using active infrared thermography (IRT) onboard an unmanned aerial vehicle (UAV) platform. The work seeks to assess the performance of small low-powered forms of excitation which are suitable for active thermography and the ability to locate subsurface defects on composites. An excitation source in multiple 250 W lamps is mounted onto a UAV and is solely battery powered with a remote trigger to power cycle them. Multiple experiments address the interference from the UAV whilst performing an active IRT inspection. The optimal distances and time required for a UAV inspection using IRT are calculated. Multiple signal processing techniques are used to analyse the composites which help locate the sub-surface defects. It was observed that a UAV can successfully carry the required sensors and equipment for an Active thermographic NDT inspection which can provide access to difficult areas. Most active thermographic inspection equipment is large, heavy, and expensive. Furthermore, using such equipment for the inspection of complex structures is time-consuming. For example, a cherry picker would be required to inspect the tail of an aircraft. This solution looks to assist engineers in inspecting complex composite structures and could potentially significantly reduce the time and cost of a routine inspection.

Towards the Automation of Infrared Thermography Inspections for Industrial Maintenance Applications

The maintenance of industrial equipment extends its useful life, improves its efficiency, reduces the number of failures, and increases the safety of its use. This study proposes a methodology to develop a predictive maintenance tool based on infrared thermographic measures capable of anticipating failures in industrial equipment. The thermal response of selected equipment in normal operation and in controlled induced anomalous operation was analyzed. The characterization of these situations enabled the development of a machine learning system capable of predicting malfunctions. Different options within the available conventional machine learning techniques were analyzed, assessed, and finally selected for electronic equipment maintenance activities. This study provides advances towards the robust application of machine learning combined with infrared thermography and augmented reality for maintenance applications of industrial equipment. The predictive maintenance system finally selected enables automatic quick hand-held thermal inspections using 3D object detection and a pose estimation algorithm, making predictions with an accuracy of 94% at an inference time of 0.006 s.

Infrared thermography detection of delamination in bottom of concrete bridge decks

Infrared thermography (IRT) has been widely used in detecting the subsurface delamination of bridge deck. However, IRT inspection on delamination zones of the bridge deck which have limited exposure to direct solar radiation (e.g., the bottom surface of the bridge deck) is rather challenging due to the relatively low thermal contrast (∆T) development in these zones. Therefore, the purpose of this study is to conduct a series of experimental studies in conjunction with numerical modeling for investigating the effectiveness of IRT in delamination detection of bridge deck components which are normally not exposed to direct solar radiation. Specially, the effects of different environmental conditions, thickness of bridge deck, and defect characteristics on the absolute thermal contrast (∆T) development were systematically investigated. The results show that IRT can effectively detect the subsurface delamination of concrete bridge deck located in regions that are not exposed to the direct solar radiation. In addition, the development of detectable thermal contrast (>0.5°C) is much dependent on the rate of change in ambient temperature with a suitable detection period between 8 am and 4 pm. Furthermore, it shows that the value of ∆T increases with the increase of bridge deck thickness and delamination size.

Long-Term Numerical Analysis of Subsurface Delamination Detection in Concrete Slabs via Infrared Thermography

One of the concerns about the use of passive Infrared Thermography (IRT) for structural health monitoring (SHM) is the determination of a favorable period to conduct the inspections. This paper investigates the use of numerical simulations to find appropriate periods for IRT-based detection of subsurface damages in concrete bridge slabs under passive heating along a 1 year of time span. A model was built using the Finite Element Method (FEM) and calibrated using the results of a set of thermographic field inspections on a concrete slab sample. The results showed that the numerical simulation properly reproduced the experimental thermographic measurements of the concrete structure under passive heating, allowing the analysis to be extended for a longer testing period. The long-term FEM results demonstrated that the months of spring and summer are the most suitable for passive IRT inspections in this study, with around 17% more detections compared to the autumn and winter periods in Brazil. By enhancing the possibility of using FEM beyond the design stage, we demonstrate that this computation tool can provide support to long-term SHM.

Evaluation and Selection of Video Stabilization Techniques for UAV-Based Active Infrared Thermography Application.

Unmanned Aerial Vehicles (UAVs) that can fly around an aircraft carrying several sensors, e.g., thermal and optical cameras, to inspect the parts of interest without removing them can have significant impact in reducing inspection time and cost. One of the main challenges in the UAV based active InfraRed Thermography (IRT) inspection is the UAV’s unexpected motions. Since active thermography is mainly concerned with the analysis of thermal sequences, unexpected motions can disturb the thermal profiling and cause data misinterpretation especially for providing an automated process pipeline of such inspections. Additionally, in the scenarios where post-analysis is intended to be applied by an inspector, the UAV’s unexpected motions can increase the risk of human error, data misinterpretation, and incorrect characterization of possible defects. Therefore, post-processing is required to minimize/eliminate such undesired motions using digital video stabilization techniques. There are number of video stabilization algorithms that are readily available; however, selecting the best suited one is also challenging. Therefore, this paper evaluates video stabilization algorithms to minimize/mitigate undesired UAV motion and proposes a simple method to find the best suited stabilization algorithm as a fundamental first step towards a fully operational UAV-IRT inspection system.

Failure Mode and Effects Analysis of CIGS Thin Film PV Modules Using Thermography Analysis and IV Measurements

This article reports on failure modes and effects analysis of CIGS thin-film modules in photovoltaic power plants. Common faults in a small photovoltaic power station consisting of 85 Copper Indium Gallium Selenide (CIGS) PV modules are analyzed under outdoor conditions using infrared thermography inspection and current-voltage measurements. The statistical features extraction of thermal images is conducted for fault detections and diagnosis in correlation with IV measurement in order to understand the relation of modules power loss with its thermal signatures. The capability of the statistical feature extraction to identify modules with more than one fault has been improved by a careful analysis of their histograms and I-V measurements. A general classification matrix of CIGS for fault detection and diagnosis using statistical features and IV measurements is established. The matrix is very useful for the operation and the maintenance planning of PV plants.

Challenges for On-Site Automated Ultrasonic Phased Array Inspection of Composite Aircraft

Aircraft maintenance represents 10-20% of the overall operating cost, and heavy C-check and D-check maintenance demands NDT inspection of 100% skin area of composite material fuselages. Delamination and low velocity Barely Visible Impact Damages (BVID) are typical in-service damages found in skin causing small to large sub-surface damages with little or no surface indication. These damages are difficult to detect during manual visual inspection. Conventional manual ultrasonic and advanced Phased Array (PA) scanning of skin followed by data interpretation and analysis for damage sizing is time consuming and cost-inefficient for on-site application; hence a potential task to be automated or robotized. Automated PA inspection by magnetic-adhesion based manipulators, crawlers and scanners have led to rapid inspection rates and lowering the cost of inspection in oil and gas sectors. The composite skin surfaces are painted, smooth and non-magnetic, which invalidates the use of current magnetic adhesion based crawlers for inspection. Despite some previous efforts, the development of automated PA inspection of skin surfaces using a mobile robot is still an unsolved problem. This work is a part of ongoing EU-H2020 project ‘CompInnova’ which aims to develop an integrated NDT approach using combined PA and Infrared Thermography inspection of the fuselage-skin through separate inspection modules mounted on a mobile robot, that will lead to cost-effective combination for on-site automated inspection. A novel vortex based wheeled mobile robot was developed for traversing over the fuselage using adhesion forces generated by means of Electric Ducted Fan (EDF) motors [4]. This robot was designed to carry a PA NDT module [4], but with restrictions on payload (1 Kg.) and minimum water couplant usage requirements. Thus this work presents the development challenges of the PA module for automated PA inspection and to meet the requirements for its integration with robot. PA inspection results obtained by robotic scanning of laminates with impact damages, are evaluated for assessing the functionality and performance of the PA module.

Pulse-heating infrared thermography inspection of bonding defects on carbon fiber reinforced polymer composites.

Non-destructive analysis of fiber reinforced polymer (FRP) composites is important for confirming the long-term safety and durability of concrete structures. In this study, a pulse-heating infrared thermography technique was used to detect and characterize bonding defects of externally bonded carbon fiber reinforced polymers (CFRP) on concrete surface structures. The CFRP composite contains various bonding defects of three different sizes located at five different depths. Sequential thermal images were obtained to describe the temperature contrast and shapes of the bonding defects. Through analysis of the maximum temperature response, we investigated the effects of defect size and depth on the defect temperature response. The relationship between the defect depth and maximum temperature response was used to quantitatively estimate the defect depth. In addition, finite element simulations were performed on the CFRP composites with bonding defects to investigate the temperature response of various defects, which showed good agreement with the experimental results. This confirms the effectiveness of the infrared thermography method to detect and characterize bonding defects of FRP composites bonded on concrete structures.

Pixel-based thermal sequence processing algorithm based on R2 fractile threshold of non-linear fitting in active infrared thermography

A novel algorithm for pixel-based thermal sequences processing based on R2 (coefficient of determination, COD) fractile threshold of non-linear fitting is proposed. Original data are obtained from active laser infrared thermography inspection to an aviation CFRP laminate with artificial defects. By fitting temperature sequence of pixels in a thermogram using the 1st-order and 2nd-order Fourier function, R2 is calculated and attached to each pixel. It is found that R2 of defective pixels is greater than that of non-defective pixels. The threshold for filtering background pixels is determined by histogram analysis of R2 set in a thermogram. Then fractile thresholds of R2 set separate pixels from defect and non-defect region. The temperature value of non-defect pixels is reduced to contrast with defect pixels. The edge of defects is sharpened, and detectable defects increase. Combining the fractile thresholds of 1st-order and 2nd-order Fourier fitting, the final processed thermograms exhibit improved detection performance evaluated by simplified probability of detection (POD) analysis and signal-to-noise ratio (SNR) calculation. In this work, the tested data show huge promotion of defect detection rate. Defect with an aspect ratio of 3.0 is detected. The combine-processed thermogram possess the maximum SNR of defective area.

Quantitative Evaluation on Electric Motor Thermal Image for Comparison Hot Spot and Measuring Point Regions

Inspection of the condition on industrial equipment becomes an urgent matter for industry. Infrared thermography inspection provides enormous benefits in preventive and predictive maintenance routines, especially for critical electrical equipment to prevent sudden damage to the equipment when the production process is underway, insofar that it impacts on the process. The result of inspection is in the form of thermal image which depicts the temperature of the electrical equipment. In general, thermal image evaluation is still analyzed manually by relying on visual reading by technicians. This would allow for errors in evaluating the image. Thus, this study used thermal images as the results of electric motor inspections, which are in hot spot and measuring point regions. Furthermore, this study aims to quantify the overheating resulted in electric motor by comparing those two regions using color entropy by Graphical User Interface (GUI) MATLAB. The study stages comprised of: zooming and object (region) cropping on color thermal image, color image histogram, calculating of color entropy (red, green, blue), and calculating of the color entropy average. The result of study on ten electric motor thermal images showed that the color entropy is higher in the measuring point region than the color entropy in the hot spot region. The average of color entropy in hot spot region were in the range of 2.9497 – 3.9578 and measuring point region were in the range of 5.1182 – 5.4489.

Practical identification of favorable time windows for infrared thermography for concrete bridge evaluation

Infrared Thermography (IRT) is one of the nondestructive inspection techniques to detect delaminations in concrete bridge decks. These defects are identified by capturing the temperature gradient of concrete surfaces. In order for this technique to be effective in damage detection, IRT inspections should be conducted at certain time windows with favorable temperature conditions to get clear temperature gradients on inspected surfaces. This study is an experimental work examining the effects of ambient environmental conditions at different times of a day to locate subsurface delaminations and voids at a shallow depth, which is an additional influencing factor. This study also attempts to figure out a relationship between ambient environmental conditions and the temperature values of concrete surfaces to estimate the best time window with appropriate environmental conditions for IRT inspections. To this end, specially designed reusable concrete test plates with different thicknesses were manufactured to collect thermocouple sensor readings. Multiple regression analyses were employed to generate prediction models that seek a relationship between environmental conditions and temperature gradients on the test plates attached to a target bridge. Regression models also utilized sensor data collected at another location different than the target bridge location. It was found out that the most important aspect of sensor data collection was to accomplish a perfect contact of test plates with concrete bridge deck surfaces to get discernible temperature gradients. When this condition is not met, data analyses yield spurious results leading to futile conclusions. On the other hand, it was also observed that prediction models generated by regression analyses followed the same pattern as that of sensor readings. This makes it possible to have prediction equations based on sensor readings to determine suitable time window for conducting IRT inspections.

Development of residual thermal stress-relieving structure of CFC monoblock target for JT-60SA divertor

Carbon fibre-reinforced carbon composite (CFC) monoblock target for JT-60SA divertor is under development towards the mass-production. CFC monoblocks, WCu interlayers and a CuCrZr cooling tube at the centre of the monoblocks were bonded by vacuum brazing in a high temperature, to a target. If residual thermal stress due to difference of thermal expansions between CFC and CuCrZr exceeds the maximum allowable stress of the CFC after the bonding, cracks are generated in the CFC monoblock and heat removal capacity of the target degrades. In this paper, new structures of the targets were proposed, to reduce residual thermal stress and to mitigate the degradation of heat removal capacity of the targets. Some measures, including slitting of the CFC monoblock aside of the cooling tube, replacement of the interlayer material and shifting the position of the cooling tube, were implemented. The effectiveness of the measures was evaluated by numerical simulations. Target mock-ups with the proposed structures were manufactured. Infrared thermography inspection and high heat flux test were carried out on the mock-ups in order to evaluate the heat removal capacity.

Advanced Progress in Diagnosing the Reliability of Electrical Equipments by Infrared Thermography

Infrared thermography was obtained more attention and accepted as an effective method in electrical preventive maintenance in account of high precision and sensitivity image features. This review elaborates the application of infrared thermography for diagnosing electrical equipment, both of the thermal anomalies and methods of measurement. Developments of the inspection techniques are remarkable for purpose of investigate the reliability of electrical equipments due to the effect of the environmental factors and equipment condition. Due to the complex analysis, various automatic diagnostic systems are proposed for faster and more accurate detection. Recent technologies of typical engineering solutions are introduced to raise the level of infrared thermography inspection.

Evaluation of Nonintrusive Active Infrared Thermography Technique to Detect Hidden Solder Ball Defects on Plastic Ball Grid Array Components

It is essential for electronics reliability to develop effective methodologies to detect hidden solder joint defects. Active infrared thermography is an alternative to X-ray detection methodologies. The limits of active infrared thermography to detect solder ball defects on plastic ball grid arrays (PBGA) components (missing, open, cracked, and head on pillow defects) are investigated here. A FEM was used to simulate the thermal phenomena during the infrared thermography inspection of a PBGA component. The FEM was proven to be temporal and spatial grids size independent. The average temperature difference (ΔT) amid regions with and without defects was used as a detectability indicator. Defects detectability was found to decrease as the number of blocking objects increases. Missing solder balls were barely detected when blocked by the substrate and moulding compound with detectability numbers close to 1 °C. Head on pillow and cracked defects were impossible to detect with a maximum ΔT = 0.6 °C. Open solder balls were not detected below two objects with a maximum ΔT = 0.3 °C. These results clearly suggest that infrared thermography can be effectively used to detect hidden missing and open solder ball defects on PBGA components composed by a substrate and a die.

Non-destructive evaluation system of metal materials according to thermal behaviours

The infrared thermography non-destructive inspection technique is a quality inspection and stability assessment method, which was used to diagnose the physical characteristics and defects by detecting the infrared ray radiated from the object without destructing it. The technique is being applied to diverse sectors. Its usage and importance increase with the development of the industrial areas that require high reliability and stability, including the semiconductor, nuclear power, defense, aerospace, and car industries. In this research, Ultrasound infrared thermography non-destructive evaluation system for metal materials according to thermal behaviours is established. To build ultrasound infrared thermography inspection system, infrared thermography test was performed by optimal design of the ultrasound excitation device and external energy control.

Infrared thermography inspection for monoblock divertor target in JT-60SA

Carbon fiber composite (CFC) monoblock divertor target is required for power handling in JT-60SA. Quality of the targets depends on a joining technology in manufacturing process. To inspect the quality of more than 900 target pieces, efficient non-destructive inspection is needed. An infrared thermography inspection (IR inspection), has been proposed by ITER and IRFM, where the quality between CFC and a cooling tube is examined by a use of transient thermal response at a rapid switch from hot to cold water flow. In JT-60SA divertor target, a screw tube will be employed to obtain high heat transfer efficiency with simple structure. Since the time response of the screw tube is much faster than that of smooth tube, it is required to confirm the feasibility of this IR inspection. Thus, the effect of joining defects on transient thermal response of the targets has been investigated experimentally by using the mock-up targets containing defects which are artificially made. It was found that the IR inspection can detect the defects. Moreover, screening criteria of IR inspection for acceptable monoblock target is discussed.