Eddy Current Pulsed Thermography Research Articles

Research on the Detection of Steel Plate Defects Based on SimAM and Twin-NMF Transfer

Pulsed eddy current thermography can detect surface or subsurface defects in steel, but in the process of combining deep learning, it is expensive and inefficient to build a complete sample of defects due to the complexity of the actual industrial environment. Consequently, this study proposes a transfer learning method based on Twin-NMF and combines it with the SimAM attention mechanism to enhance the detection accuracy of the target domain task. First, to address the domain differences between the target domain task and the source domain samples, this study introduces a Twin-NMF transfer method. This approach reconstructs the feature space of both the source and target domains using twin non-negative matrix factorization and employs cosine similarity to measure the correlation between the features of these two domains. Secondly, this study integrates a parameter-free SimAM into the neck of the YOLOv8 model to enhance its capabilities in extracting and classifying steel surface defects, as well as to alleviate the precision collapse phenomenon associated with multi-scale defect recognition. The experimental results show that the proposed Twin-NMF model with SimAM improves the detection accuracy of steel surface defects. Taking NEU-DET and GC10-DET as source domains, respectively, in the ECTI dataset, mAP@0.5 reaches 99.3% and 99.2%, and the detection accuracy reaches 98% and 98.5%.

Characterization of Multi-Layer Rolling Contact Fatigue Defects in Railway Rails Using Sweeping Eddy Current Pulse Thermal-Tomography

Railways play a pivotal role in national economic development, freight transportation, national defense, and regional connectivity. The detection of rolling contact fatigue (RCF) defects in rail tracks is essential for railway safety and maintenance. Due to its efficiency and non-contact capability in detecting surface and near-surface defects, Eddy Current Pulsed Thermography (ECPT) has garnered significant attention from researchers. However, detecting multi-layer RCF defects remains a challenge. This paper introduces a sweeping Eddy Current Pulsed Thermal-Tomography system (ECPTT) to detect multi-layer RCF defects effectively. This system utilizes varying excitation frequencies to heat defects, altering skin depth and facilitating feature extraction to distinguish multi-layer RCF defects. Skewness and thermographic signal reconstruction (TSR) values are employed as features in the experiments. These features are qualitatively analyzed to differentiate the layers and depths of multi-layer RCF defects. Additionally, five different coils were compared and analyzed quantitatively. The results indicate that the ECPTT system can effectively detect and distinguish multi-layer RCF defects, thereby providing more detailed defect information and enhancing railway safety and maintenance efficiency.

Surface Defect 3-D Profile Reconstruction Using Eddy Current Pulsed Thermography

Surface Defect 3-D Profile Reconstruction Using Eddy Current Pulsed Thermography

Induced Current Thermo-Electrical Impedance Tomography for Nonferromagnetic Metal Material Surface Defect Profile Reconstruction

Nonferromagnetic metal materials are widely used in industry. Defects generated during manufacture and use may lead to serious accidents. The defect reconstruction is important for nondestructive evaluation. Eddy current pulsed thermography (ECPT) is a well-known nondestructive testing and evaluation method, but hardly reconstruct fully defect profile. Electrical impedance tomography (EIT) shows promising potential in defect profile reconstruction, but suffers from electrode number limitation. In this paper, EIT is introduced to ECPT image sequences processing, and a new method, induced current thermo-electrical impedance tomography (ICTEIT), is developed. The proposed method takes each infrared camera pixel as a virtual electrode, captures the electric current distribution with spatial resolution as high as camera, and reconstructs conductivity distribution at pixel level from which the defect profile can be identified. Experiments with different defects are carried out to verify the performance of the proposed method.

One-dimensional deep convolutional autoencoder active infrared thermography: Enhanced visualization of internal defects in FRP composites

Fiber-reinforced polymer (FRP) composites have been widely applied in different industrial fields, thereby necessitating the employment of non-destructive testing (NDT) methods to ensure structural integrity and safety. Active infrared thermography (AIRT) is a fast and cost-efficient NDT technique for inspecting FRP composites. However, this method is easily affected by factors such as inhomogeneous heating, leading to a low level of visualization of defects. To address this issue, this study proposes a novel method called one-dimensional deep convolutional autoencoder active infrared thermography (1D-DCAE-AIRT) to enhance the visualization of internal defects in FRP composites. This method first preprocesses the thermal image sequence acquired by AIRT inspections. Subsequently, high-level thermal features at the pixel level are extracted from the aforementioned preprocessed thermal image sequence using a designed one-dimensional deep convolutional autoencoder (1D-DCAE) model. Finally, the extracted high-level thermal features are employed to generate enhanced visualization results that exhibit improved defect visibility. The results of three kinds of AIRT (eddy current pulsed thermography, flash thermography, and vibrothermography) experiments on FRP composite specimens with artificially introduced defects show that 1D-DCAE-AIRT can effectively enhance the visualization of internal defects. The enhancement effect is better than the conventional techniques of fast Fourier transform (FFT), principal component analysis (PCA), independent component analysis (ICA), and partial least-squares regression (PLSR).

Comparison Research on Characterization and Evaluation Approaches for Paint Coated Corrosion Using Eddy Current Pulsed Thermography.

Paint coated corrosion detection and evaluation is a big challenge for steel performance and structure health. Eddy current pulsed thermography (ECPT) technique is investigated because it can reflect the corrosion physical properties through paint coating by the infrared signal. This paper proposes skewness method, which presents the feature of temperature curve's shape automatically, and compares it with principal component analysis (PCA), phase analysis, and kurtosis feature extraction methods for paint coated corrosion characterization and evaluation. The averaged skewness shows the best sensitivity for 0-6 months corrosion. The normalized second principal component (PC) presents good sensitivity and the best measurement scale for corroded time. Furthermore, the temperature curve analysis proves that the electrical conductivity dominates the induced heating and heat distribution. The corrosion height is utilized to explain why ECPT technique is valid within 10 months corroded time. ECPT technique is proved as a smart sensor system for paint coated corrosion detection and characterization.

A new probe‐based electromagnetic non‐destructive testing method for carbon fiber‐reinforced polymers utilizing eddy current loss measurements

AbstractEddy current testing (ECT) is a widely used technique for detecting defects in carbon fiber‐reinforced polymers (CFRPs) at high frequencies. However, designing high‐frequency systems can be challenging, and high noise levels can be problematic. To address these issues, non‐destructive testing (NDT) methods that can detect CFRP defects at lower frequencies must be explored. In this study, a novel NDT method for detecting defects in CFRP at low frequencies was proposed. This method is an alternative to pulsed eddy current thermography and can achieve similar detection and imaging results at low frequencies. Furthermore, the developed method has a simpler structure and is easier to implement. This method uses the eddy current loss to characterize the thermal power of the eddy current in CFRP. A new probe was used to detect the eddy current loss to inspect the CFRP. The effectiveness of the proposed method was experimentally verified using different types of CFRP samples based on the C‐scan technology. The scanned images demonstrate that the developed method can provide appropriate detection results up to a frequency of 750 kHz. Furthermore, rotation experiments were performed to detect the fiber direction; the proposed method successfully detected the fiber direction up to 750 kHz. Thus, the proposed method can improve the detection of defects and enhance the longevity of CFRP components. Future work will focus on enhancing the sensitivity of this method at even lower frequencies.

Influence of water film on subsurface defect detection using eddy current pulsed thermography

Eddy current pulsed thermography (ECPT) is an effective non-destructive testing (NDT) technique for detecting flaws in metallic materials. However, due to the low radiation and high reflection properties of metallic materials, as well as the inhomogeneous emission caused by the complex states of the material surface, the detection of subsurface defects becomes difficult. In this paper, the physical mechanisms underlying the interference of the thin water film in ECPT detection are studied. Two static comparative experiments have been carried out on the ferromagnetic plate with several artificial subsurface defects. In the first comparative experiment, a high radiation point has been artificially added near the defects. The experimental results showed that water film could eliminate the influence of inhomogeneous emission. In the second experiments, the effectiveness of water film in improving the detectability of subsurface defects has been demonstrated and the subsurface defects with a maximum buried depth of 0.4 mm can be detected.

Non-destructive evaluation of weld defect with coating using electromagnetic induction thermography

ABSTRACT The weld inspection process under coating is complex and labour-dependent. Therefore, it is of great engineering significance to study weld defect inspection. Based on eddy current pulsed thermography (ECPT) method, this work theoretically analysed the mechanism of weld defect detection undercoating and proposed an inductor suitable for defect detection under weld coating to achieve non-contact, rapid and visual detection results. The simulation model for weld defect detection under coating was constructed, and the effect of coating on weld crack inspection was investigated. However, weld defect test blocks with different coating, thicknesses were verified by experiment, and the thermal characteristics of the circular hole area were analysed. The results demonstrate that the proposed method is effective in weld defect detection, which provides a promising way to extend the application of ECPT to weld defect inspection.

Emissivity Correction and Thermal Pattern Reconstruction in Eddy Current Pulsed Thermography

Emissivity variations are one of the most critical challenges in thermography technologies; this is due to the temperature calculation strongly depending on emissivity settings for infrared signal extraction and evaluation. This paper describes an emissivity correction and thermal pattern reconstruction technique based on physical process modelling and thermal feature extraction, for eddy current pulsed thermography. An emissivity correction algorithm is proposed to address the pattern observation issues of thermography in both spatial and time domains. The main novelty of this method is that the thermal pattern can be corrected based on the averaged normalization of thermal features. In practice, the proposed method brings benefits in enhancing the detectability of the faults and characterization of the materials without the interference of the emissivity variation problem at the object's surfaces. The proposed technique is verified in several experimental studies, such as the case-depth evaluation of heat-treatment steels, failures, and fatigues of gears made of the heat-treated steels that are used for rolling stock applications. The proposed technique can improve the detectability of the thermography-based inspection methods and would improve the inspection efficiency for high-speed NDT&E applications, such as rolling stock applications.

Review on Detection of Internal Defects in CFRP Strengthened Steel Structures Using Eddy Current Pulsed Thermography

Abstract: This study investigates the effectiveness of the current pulse thermal technique (ESRT) in detecting internal defects in СFRР reinforced metal structures. This study covers various internal defects of FRР reinforced steel structures, including cracks, small tracks in СFRР, metal cracks / inter-steel cracks. We theoretically analyze the effect of these problems on the magnetic and thermal response of structures. To confirm the results of the theater, statistical and diagnostic tests were performed on steel plates reinforced with СFRР with various defects. Numerical and diagnostic results show that EСРТ can estimate observed deformations and show low temperature regions in EСРТ thermograms. In addition to the thermal characteristics of the thermograms, the appearance of the temperature profile and changes in temperature are also examined for reanalysis of the thermal analysis. In addition, based on the test results, the effectiveness of EСRT to restore the size and scope of errors was demonstrated. Overall, theoretical, numerical and experimental results show that EСRT is effective in diagnosing internal defects in CFRP-reinforced steel structures

Deep Transfer Learning-Based Pulsed Eddy Current Thermography for Crack Defect Detection

Deep Transfer Learning-Based Pulsed Eddy Current Thermography for Crack Defect Detection

Visualization of defects in CFRP-reinforced steel structures using improved eddy current pulsed thermography

Carbon Fiber Reinforced Plastic (CFRP) has been increasingly utilized to repair damaged steel structures, and inspection of the resulted CFRP-reinforced steel structures is crucial but still challenging. Different physical characteristics of steel, CFRP and epoxy resin bring difficulties to accurate detection and clear visualization of internal defects in the hybrid structures. In this study, eddy current pulsed thermography (ECPT) was exploited to inductively heat the inspected structures and a stacked autoencoder (SAE) model was constructed to extract pixel-wise thermal features and generate images for visualization of internal defects. Experimental results on specimens with prefabricated debondings, delaminations and cracks validated efficiency of the proposed method. Clear visualization of defects provided by the proposed method is greatly beneficial for accurate detection and efficient information retrieval of internal defects in structures. In future studies, performance of the proposed method for other kinds of defects in CFRP-reinforced steel structures should be investigated.

Automated identification of front/rear surface cracks in ferromagnetic metals based on eddy current pulsed thermography

Surface cracks may significantly degrade performance of metal components and may lead to failure of constructions. The present study develops a method for automated identification of front/rear surface cracks in ferromagnetic metals. The method employs eddy current pulsed thermography (ECPT) to induce eddy currents and Joule heat in the inspected metal parts. Front/rear surface cracks interfere both eddy current distribution and the following heat conduction, which results in abnormal thermal response on the metal surface. A local region-based strategy is developed to identify these crack regions from the ECPT inspection data through a supervised classification procedure. Due to this strategy, statistical features of local regions in the inspected surface are extracted and are used as the classification features to accomplish crack recognition. A multi-class support vector machine (SVM) classifier is constructed to segment the local regions into crack regions, sound regions and insufficiently-heated regions. To evaluate the proposed approach, experiments were implemented on steel plates with prefabricated notches of various sizes. Both front surface cracks and rear surface cracks were involved in these experiments. Experimental results verify the effectiveness of the approach for automated identification of front/rear surface cracks in metal parts.

Characterisation and evaluation of paint-coated marine corrosion in carbon steel using eddy current pulsed thermography

Marine atmospheric corrosion is a major challenge for carbon steel. Eddy current pulsed thermography (ECPT) can characterise defects under coating, but it is necessary to consider samples' surface geometry. This paper proposes a statistical approach for paint-coated S275 steel corrosion evaluation based on ECPT. The thermal response curve skewness of each point is utilised to reveal the corrosion region. The normalised skewness avoids the influence of emissivity anisotropy and surface geometry; corrosion can be evaluated by taking excitation coil skewness as a reference. In the results, the differential skewness drops monotonously with corrosion aggravation in 6 months, decreasing more than 90% from the uncorroded zone to the 3-month corrosion sample. The approach provides high sensitivity and detectability in paint-coated corrosion evaluation.

Analysis of the RCF crack detection phenomenon based on induction thermography.

Eddy current pulsed thermography (ECPT) is used to detect rolling contact fatigue (RCF) cracks on the rail. It is observed that some of the cracks disappear in the thermal image with the increase of heating time. Based on the finite element method, with double cracks as the basic unit, three different crack models are established, and the mutual disturbance relationship between the double cracks is discussed based on the eddy current distribution and thermal diffusion process. The simulation and experimental results show that different crack models are affected by thermal diffusion in different heating stages to different degrees, and the time of the crack thermal image disappearance is obtained. According to the above conclusions, the RCF cracks are extracted and classified based on the influence of thermal diffusion. The possibility of rail condition assessment and maintenance based on the disappearance time is explained.

Quantitative mapping of depth profile of fatigue cracks using eddy current pulsed thermography assisted by PCA and 2D wavelet transformation

Fatigue cracks are widely generated in numerous engineering components under alternating loads, whose sudden brittle fracture would seriously shorten the service life and cause significant hazards. A large number of nondestructive testing (NDT) techniques have been utilized to quantitatively evaluate the fatigue cracks, however, most of these methods are difficult to be used for a full characterization of the cracks’ depth profile. The eddy current pulsed thermography (ECPT) as a promising NDT technique shows great potential for fatigue crack evaluation due to its high detection efficiency, large inspection area, and high spatial resolution. In this article, a ferrite yoke was utilized to generate a homogeneous eddy current field, and as such to enhance the detection sensitivity and to provide a wide open-view area for the camera. The hybrid processing strategy combining principal component analysis (PCA) and 2D wavelet transformation was proposed to extract spatial features of the thermographic image sequences. Firstly, numerical results demonstrate that the profiles of slot cracks can be identified directly from the thermographic images in the early heating stage. Afterward, the ECPT experiments on slot cracks with different depth profiles demonstrate the feasibility of the hybrid processing strategy. Then, three fatigue cracks were manufactured through the three-point bending method and detected by ECPT. The results of the hybrid processing method indicate that these three fatigue cracks have elliptical profiles. Finally, the authentic depth profile of a fatigue crack with an elliptical shape is extracted through a destructive experiment. The comparison results between the real depth profile and the profile characterized through the proposed strategy show great consistency and validate the superiority of the proposed strategy.

Transient thermal pattern separation and detection of conductive defects in composite insulators using eddy current pulsed thermography

Composite insulators are prone to internal conductive defects owing to their long-term high-voltage operation and outdoor aging. In this study, a method based on eddy current pulsed thermography (ECPT) combined with feature extraction transform algorithms is proposed for transient thermal pattern separation and defect detection in composite insulators with internal conductive defects. The principles of this method, including electromagnetic heating, thermal diffusion, and transient thermal response extraction, are investigated. Conductive defects in insulators have unique thermal patterns that differ from those of non-conductive defects in conductive materials. In this study, the original transient thermal responses of conductive defects are analyzed and compared with those of non-conductive defects. The effectiveness of the transient thermal pattern separation of this method is verified through experimental studies. Different feature extraction transform methods are utilized and compared to separate different thermal transient patterns and perform quantitative evaluations. It is found that ECPT combined with feature extraction transform algorithms can provide a visual and effective method to detect internal conductive defects in composite insulators.

Potential of Eddy Current Pulsed Thermography as a Nondestructive Testing Method

The parts of working machinery have always suffered from various working conditions in the process of use (e.g., high temperature, high humidity, and high pressure) that may generate flaws, pitting, thermal fatigue crack and other types of defects on the surface or inside. Because these tiny defects carry big safety risks, regular detection is necessary to ensure the reliability of the production equipment.

Depth quantification of rolling contact fatigue crack using skewness of eddy current pulsed thermography in stationary and scanning modes

Eddy current pulsed thermography (ECPT) has been used in the characterization of Rolling Contact Fatigue (RCF) cracks in rail by taking advantage of electromagnetic thermal execution. However, quantitative estimation of depths of RCF defects remains challenging due to volume eddy current heating and lateral thermal diffusion under the stationary and scanning inspections. This work proposes math-physic modelling of skewness to quantitatively characterize crack depth in a rail sample. In particular, a physical model linked instrument describing the accumulation of Joule heating via eddy current accompanied by heat diffusion and mathematical skewness has been established. A comparison between the accuracy of crack depth determination in stationary and scanning modes was carried out in terms of the thermal response and skewness. Moreover, the effects of crack types on depth quantification have been analyzed. The comparative experimental results indicated that the skewness under stationary conditions is more robust against the noise, and this has verified the efficiency for quantifying RCF cracks in stationary and scanning modes.