Pub Date : 2022-12-01DOI: 10.1784/insi.2022.64.12.688
Jiarui Feng, Entao Yao, Ping Wang, Yuxia Shi
Remanence detection is a technique of electromagnetic non-destructive testing (NDT). This paper studies a quantitative detection method for surface cracks of ferromagnetic materials based on remanence. The finite element analysis software COMSOL Multiphysics was used to establish the remanence detection model and the 'moving grid' function was used to realise the simulation of the remanence signal. The leakage magnetic field occurs due to the distortion of the magnetic induction lines near the surface cracks after ferromagnetic materials are magnetised. Remanence detection uses the leakage magnetic field to detect cracks. The relationship of the leakage magnetic field versus the crack depth and width was analysed using the magnetic dipole model. The relationship between the crack size and the remanence signal was verified by measuring the surface remanence signal of cracks of different sizes. The characteristic parameters related to the crack size were extracted and the regression model between the characteristic parameters and the crack size was established. For the remanence detection, the maximum error of width prediction was 16.25% and the maximum error of depth prediction was 18.48%. For the magnetic flux leakage (MFL) detection, the maximum error of width prediction was 12.1% and the maximum error of depth prediction was 12.32%. Under the same experimental conditions, the maximum error of crack width measurement of remanence detection was 4.15% larger than that of MFL detection and the maximum error of depth was 6.16% larger than that of MFL detection.
{"title":"Study on surface crack detection of ferromagnetic materials based on remanence","authors":"Jiarui Feng, Entao Yao, Ping Wang, Yuxia Shi","doi":"10.1784/insi.2022.64.12.688","DOIUrl":"https://doi.org/10.1784/insi.2022.64.12.688","url":null,"abstract":"Remanence detection is a technique of electromagnetic non-destructive testing (NDT). This paper studies a quantitative detection method for surface cracks of ferromagnetic materials based on remanence. The finite element analysis software COMSOL Multiphysics was used to establish the\u0000 remanence detection model and the 'moving grid' function was used to realise the simulation of the remanence signal. The leakage magnetic field occurs due to the distortion of the magnetic induction lines near the surface cracks after ferromagnetic materials are magnetised. Remanence detection\u0000 uses the leakage magnetic field to detect cracks. The relationship of the leakage magnetic field versus the crack depth and width was analysed using the magnetic dipole model. The relationship between the crack size and the remanence signal was verified by measuring the surface remanence signal\u0000 of cracks of different sizes. The characteristic parameters related to the crack size were extracted and the regression model between the characteristic parameters and the crack size was established. For the remanence detection, the maximum error of width prediction was 16.25% and the maximum\u0000 error of depth prediction was 18.48%. For the magnetic flux leakage (MFL) detection, the maximum error of width prediction was 12.1% and the maximum error of depth prediction was 12.32%. Under the same experimental conditions, the maximum error of crack width measurement of remanence detection\u0000 was 4.15% larger than that of MFL detection and the maximum error of depth was 6.16% larger than that of MFL detection.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123182351","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1784/insi.2022.64.12.695
Zhaoming Zhou, Chunfu Yang, Liyan Liu, Donghong Zhao, Kai Li
The overlay panels of spent fuel pools of nuclear power plants can easily become corroded and produce micro-crack defects. Surface crack defects tend to expand vertically, horizontally and obliquely, causing damage and fracture to the overlay panels and welds of spent fuel pools. Traditional non-destructive testing (NDT) cannot complete underwater testing in real time. In order to improve the timeliness of crack detection and shorten the inspection period, research on accurate inspection technology for surface cracks in the overlay panels of spent fuel pools is carried out in this paper based on alternating current field measurement (ACFM) and the weld defect detection process for the cladding panels of spent pools is optimised. In this work, different types of artificial defect are assumed and the distortion of the magnetic field characteristic signal caused by the defects is studied. The characteristics of magnetic field signals generated in different defect regions are studied by establishing a defect electromagnetic detection model for numerical calculation. Finally, experimental and numerical results are compared and analysed. The results show that ACFM can be used to quickly and effectively inspect for cracks in the base material, weld and interface of spent fuel pool overlay panels and it has the characteristics of accuracy, high resolution, high sensitivity and low delay. The research results, which have good application value, provide technical support for electromagnetic inspection of latent cracks in field spent fuel pools and early crack warning of underwater structural defects.
{"title":"Welding defect detection in nuclear power plant spent fuel pool panels based on alternating current field measurement: experimental and finite element analysis","authors":"Zhaoming Zhou, Chunfu Yang, Liyan Liu, Donghong Zhao, Kai Li","doi":"10.1784/insi.2022.64.12.695","DOIUrl":"https://doi.org/10.1784/insi.2022.64.12.695","url":null,"abstract":"The overlay panels of spent fuel pools of nuclear power plants can easily become corroded and produce micro-crack defects. Surface crack defects tend to expand vertically, horizontally and obliquely, causing damage and fracture to the overlay panels and welds of spent fuel pools. Traditional\u0000 non-destructive testing (NDT) cannot complete underwater testing in real time. In order to improve the timeliness of crack detection and shorten the inspection period, research on accurate inspection technology for surface cracks in the overlay panels of spent fuel pools is carried out in\u0000 this paper based on alternating current field measurement (ACFM) and the weld defect detection process for the cladding panels of spent pools is optimised. In this work, different types of artificial defect are assumed and the distortion of the magnetic field characteristic signal caused by\u0000 the defects is studied. The characteristics of magnetic field signals generated in different defect regions are studied by establishing a defect electromagnetic detection model for numerical calculation. Finally, experimental and numerical results are compared and analysed. The results show\u0000 that ACFM can be used to quickly and effectively inspect for cracks in the base material, weld and interface of spent fuel pool overlay panels and it has the characteristics of accuracy, high resolution, high sensitivity and low delay. The research results, which have good application value,\u0000 provide technical support for electromagnetic inspection of latent cracks in field spent fuel pools and early crack warning of underwater structural defects.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"89 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"126227250","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1784/insi.2022.64.12.702
Ronggui Zhu, Dandan Chi, X. Zhan
In the present study, damage evolution in rolled Al-Zn-Mg alloy and its welds is evaluated using the acoustic emission (AE) method and crack initiation is detected using digital imaging during fatigue tests. The AE characteristics and source mechanisms are analysed based on microstructural and fractographic observations. The experimental results show that AE energies are effective indicators for detecting fatigue crack initiation in Al-Zn-Mg alloys. The results obtained were verified through digital images of the notch tip region of the Al-Zn-Mg alloy samples. For small percentages of the applied load range close to the peak load, the AE count rates show a reasonable correlation with the crack propagation rates. These correlations can be applied to predict the remaining service life of fatigue-damaged structures. The analyses performed demonstrate that the AE technique is sensitive to variations in the fracture mode and could be applied to monitor fatigue damage evolution in welded structures.
{"title":"In-situ fatigue damage monitoring of rolled Al-Zn-Mg alloy using an advanced acoustic emission technique","authors":"Ronggui Zhu, Dandan Chi, X. Zhan","doi":"10.1784/insi.2022.64.12.702","DOIUrl":"https://doi.org/10.1784/insi.2022.64.12.702","url":null,"abstract":"In the present study, damage evolution in rolled Al-Zn-Mg alloy and its welds is evaluated using the acoustic emission (AE) method and crack initiation is detected using digital imaging during fatigue tests. The AE characteristics and source mechanisms are analysed based on microstructural\u0000 and fractographic observations. The experimental results show that AE energies are effective indicators for detecting fatigue crack initiation in Al-Zn-Mg alloys. The results obtained were verified through digital images of the notch tip region of the Al-Zn-Mg alloy samples. For small percentages\u0000 of the applied load range close to the peak load, the AE count rates show a reasonable correlation with the crack propagation rates. These correlations can be applied to predict the remaining service life of fatigue-damaged structures. The analyses performed demonstrate that the AE technique\u0000 is sensitive to variations in the fracture mode and could be applied to monitor fatigue damage evolution in welded structures.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"37 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121495588","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-12-01DOI: 10.1784/insi.2022.64.12.680
Fengmiao Tu, Min Wei, Jun Liu, Lixia Jiang, Jia Zhang
Defect depth inversion is generally considered as a challenge in magnetic flux leakage (MFL) testing and evaluation because of its strong non-linearity and low prediction accuracy. Current inversion models focus on the inversion accuracy of specific datasets, ignoring consideration of the generalisation ability of inversion models under different conditions. In order to solve such problems, this paper proposes a novel pipeline defect inversion method based on a Bayesian regularisation neural network (BRNN) model. This method consists of two parts. Firstly, three domain features are extracted and a Boruta algorithm is introduced to reduce the feature dimension and obtain the best feature subset. Secondly, in order to approximate the complex non-linear relationship between multi-dimensional features and defect depth, a back-propagation neural network (BPNN) model based on Levenberg-Marquardt optimisation and a Bayesian learning algorithm is constructed. The model can effectively find a close global minimum and overcome the phenomena of overfitting and overtraining. In order to evaluate the performance of the proposed defect inversion method, a comparative experiment is carried out with other well-known inversion algorithms. The results obtained confirm that the inversion method can improve the prediction accuracy of defect depth. More importantly, this method enhances the generalisation ability of defect inversion problems with different sample sets.
{"title":"Metal-loss defect depth inversion in oil and gas pipelines based on Bayesian regularisation neural network","authors":"Fengmiao Tu, Min Wei, Jun Liu, Lixia Jiang, Jia Zhang","doi":"10.1784/insi.2022.64.12.680","DOIUrl":"https://doi.org/10.1784/insi.2022.64.12.680","url":null,"abstract":"Defect depth inversion is generally considered as a challenge in magnetic flux leakage (MFL) testing and evaluation because of its strong non-linearity and low prediction accuracy. Current inversion models focus on the inversion accuracy of specific datasets, ignoring consideration\u0000 of the generalisation ability of inversion models under different conditions. In order to solve such problems, this paper proposes a novel pipeline defect inversion method based on a Bayesian regularisation neural network (BRNN) model. This method consists of two parts. Firstly, three domain\u0000 features are extracted and a Boruta algorithm is introduced to reduce the feature dimension and obtain the best feature subset. Secondly, in order to approximate the complex non-linear relationship between multi-dimensional features and defect depth, a back-propagation neural network (BPNN)\u0000 model based on Levenberg-Marquardt optimisation and a Bayesian learning algorithm is constructed. The model can effectively find a close global minimum and overcome the phenomena of overfitting and overtraining. In order to evaluate the performance of the proposed defect inversion method,\u0000 a comparative experiment is carried out with other well-known inversion algorithms. The results obtained confirm that the inversion method can improve the prediction accuracy of defect depth. More importantly, this method enhances the generalisation ability of defect inversion problems with\u0000 different sample sets.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"59 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-12-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127744196","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-11-01DOI: 10.1784/insi.2022.64.11.633
R. Sanderson, A. Sanderson, K. Akowua, H. Livesey
The feasibility of a new approach for pipe inspection has been explored using digital twins to enhance guided wave inspection. Guided wave inspection is well established in the oil & gas industry to remotely screen long lengths of predominately straight pipeline for corrosion. However, the inspection of complex pipe geometries remains a challenge. Nuclear fusion facilities are one such potential application. Fusion reactors have a network of many kilometres of service pipes with complex features, including multiple pipe bends. Some of these pipes could be used for actively cooling components such as the first wall and divertor. Guided ultrasonic wave inspection has the significant advantage of offering 100% coverage of the pipe wall over tens of metres of pipe from a remote test location. This is a highly attractive feature, particularly in the nuclear industry where it is important that human presence in high-risk areas is prohibited due to high radiation doses and temperatures. In this work, finite element wave propagation models have been investigated as digital twins of fusion reactor components. The models have been used to calculate bespoke excitation signals that will allow for full volumetric inspections of these complex pipes to be carried out from a remote location. For the first time, a digital twin technique has been developed that is predicted to successfully correct the distortion in guided wave signals caused by multiple pipe bends. The technique is predicted to yield an order of magnitude improvement in detection capability over conventional guided wave inspection. The digital twin technique presented here therefore shows significant promise for the future inspection of nuclear fusion power plant pipes.
{"title":"Development of digital tools to enable remote ultrasonic inspection of fusion ractor in-vessel components","authors":"R. Sanderson, A. Sanderson, K. Akowua, H. Livesey","doi":"10.1784/insi.2022.64.11.633","DOIUrl":"https://doi.org/10.1784/insi.2022.64.11.633","url":null,"abstract":"The feasibility of a new approach for pipe inspection has been explored using digital twins to enhance guided wave inspection. Guided wave inspection is well established in the oil & gas industry to remotely screen long lengths of predominately straight pipeline for corrosion. However,\u0000 the inspection of complex pipe geometries remains a challenge. Nuclear fusion facilities are one such potential application. Fusion reactors have a network of many kilometres of service pipes with complex features, including multiple pipe bends. Some of these pipes could be used for actively\u0000 cooling components such as the first wall and divertor. Guided ultrasonic wave inspection has the significant advantage of offering 100% coverage of the pipe wall over tens of metres of pipe from a remote test location. This is a highly attractive feature, particularly in the nuclear industry\u0000 where it is important that human presence in high-risk areas is prohibited due to high radiation doses and temperatures. In this work, finite element wave propagation models have been investigated as digital twins of fusion reactor components. The models have been used to calculate bespoke\u0000 excitation signals that will allow for full volumetric inspections of these complex pipes to be carried out from a remote location. For the first time, a digital twin technique has been developed that is predicted to successfully correct the distortion in guided wave signals caused by multiple\u0000 pipe bends. The technique is predicted to yield an order of magnitude improvement in detection capability over conventional guided wave inspection. The digital twin technique presented here therefore shows significant promise for the future inspection of nuclear fusion power plant pipes.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"92 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"123416576","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1784/insi.2022.64.10.573
Jiaxing Xin, Jinzhong Chen, Xiaolong Li, R. He, Hongwu Zhu
Deformation is one of the leading causes of oil and gas pipeline accidents, affecting pipeline transportation efficiency and operational safety. This paper proposes a pipeline deformation detection method and prediction models based on alternating current magnetisation (ACM) technology. Firstly, the mechanism of pipeline deformation detection based on ACM technology is introduced and mathematical models are proposed to evaluate the deformation length and height using magnetic detection signals. Next, finite element models of detection signals for deformations with various lengths and heights are analysed and original signal waveforms are obtained. Furthermore, linear and polynomial fitting mathematical models are developed to invert the deformation length and height using the measured peak signal and L' (distorted signal length) value. Finally, experiments are conducted to demonstrate that the length and depth of a deformation can be estimated by linear and polynomial models with tolerable errors. The proposed approach combining ACM and a prediction model is verified to size deformation in pipeline inspection quantitatively.
{"title":"A prediction model for oil and gas pipeline deformation based on ACM inspection signal waveforms","authors":"Jiaxing Xin, Jinzhong Chen, Xiaolong Li, R. He, Hongwu Zhu","doi":"10.1784/insi.2022.64.10.573","DOIUrl":"https://doi.org/10.1784/insi.2022.64.10.573","url":null,"abstract":"Deformation is one of the leading causes of oil and gas pipeline accidents, affecting pipeline transportation efficiency and operational safety. This paper proposes a pipeline deformation detection method and prediction models based on alternating current magnetisation (ACM) technology.\u0000 Firstly, the mechanism of pipeline deformation detection based on ACM technology is introduced and mathematical models are proposed to evaluate the deformation length and height using magnetic detection signals. Next, finite element models of detection signals for deformations with various\u0000 lengths and heights are analysed and original signal waveforms are obtained. Furthermore, linear and polynomial fitting mathematical models are developed to invert the deformation length and height using the measured peak signal and L' (distorted signal length) value. Finally, experiments\u0000 are conducted to demonstrate that the length and depth of a deformation can be estimated by linear and polynomial models with tolerable errors. The proposed approach combining ACM and a prediction model is verified to size deformation in pipeline inspection quantitatively.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"130555877","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1784/insi.2022.64.10.589
T. Nguyễn, Helsin Wang, Chung-Yue Wang
Currently, flexural wave impulse response (IR) tests, which provide better accessibility for inspecting the partially exposed foundations of in-service bridges or buildings, are not used for frequency analysis due to the dispersion characteristics of bending waves at low frequencies. Despite a drawback at low frequencies, both the velocity and frequency span become constant in the high-frequency range. This article uses frequency spectrum-based analysis to evaluate the lengths of three partially embedded model concrete piles subject to lateral impact. The empirical mode decomposition (EMD) approach is used to determine the lower bound frequency, where two requirements, ie constant velocity and regular frequency span, can be fulfilled in order to apply the one-dimensional (1D) wave concept at high frequencies. Beyond the lower bound frequency, the 1D wave concept is reasonably used to predict the pile lengths, with an estimated error below 5% based on frequency analysis.
{"title":"Application of empirical mode decomposition to determine pile lengths subject to lateral impact","authors":"T. Nguyễn, Helsin Wang, Chung-Yue Wang","doi":"10.1784/insi.2022.64.10.589","DOIUrl":"https://doi.org/10.1784/insi.2022.64.10.589","url":null,"abstract":"Currently, flexural wave impulse response (IR) tests, which provide better accessibility for inspecting the partially exposed foundations of in-service bridges or buildings, are not used for frequency analysis due to the dispersion characteristics of bending waves at low frequencies.\u0000 Despite a drawback at low frequencies, both the velocity and frequency span become constant in the high-frequency range. This article uses frequency spectrum-based analysis to evaluate the lengths of three partially embedded model concrete piles subject to lateral impact. The empirical mode\u0000 decomposition (EMD) approach is used to determine the lower bound frequency, where two requirements, ie constant velocity and regular frequency span, can be fulfilled in order to apply the one-dimensional (1D) wave concept at high frequencies. Beyond the lower bound frequency, the 1D wave\u0000 concept is reasonably used to predict the pile lengths, with an estimated error below 5% based on frequency analysis.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"14 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"121902270","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
In this paper, a magnetic memory detection device under weak magnetic field excitation is designed to better solve the problem of weak magnetic memory detection signals and susceptibility to other factors. In order to reduce the noise in the original signal, a noise reduction method combining local mean decomposition and wavelet transform (LMDW) is proposed. Pseudo-colour transformation is used to enhance the greyscale image after cubic spline interpolation. Finally, a convolutional neural network (CNN) is designed to identify broken wire. Moreover, compared with the support vector machine (SVM) algorithm, the recognition rate of the CNN is 35.8% higher than that of the SVM under the condition that the allowable error is 0. The experimental results show that the system has high detection sensitivity and remains effective for small defects. The filtering algorithm has a better effect on noise removal and improves the signal-to-noise ratio (SNR). The CNN has good recognition ability to identify defects.
{"title":"Application of a convolutional neural network in wire rope magnetic memory testing","authors":"Juwei Zhang, Bing Li, Zengguang Zhang, Qihang Chen","doi":"10.1784/insi.2022.64.10.566","DOIUrl":"https://doi.org/10.1784/insi.2022.64.10.566","url":null,"abstract":"In this paper, a magnetic memory detection device under weak magnetic field excitation is designed to better solve the problem of weak magnetic memory detection signals and susceptibility to other factors. In order to reduce the noise in the original signal, a noise reduction method\u0000 combining local mean decomposition and wavelet transform (LMDW) is proposed. Pseudo-colour transformation is used to enhance the greyscale image after cubic spline interpolation. Finally, a convolutional neural network (CNN) is designed to identify broken wire. Moreover, compared with the\u0000 support vector machine (SVM) algorithm, the recognition rate of the CNN is 35.8% higher than that of the SVM under the condition that the allowable error is 0. The experimental results show that the system has high detection sensitivity and remains effective for small defects. The filtering\u0000 algorithm has a better effect on noise removal and improves the signal-to-noise ratio (SNR). The CNN has good recognition ability to identify defects.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"39 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"127383170","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1784/insi.2022.64.10.560
M. S. Alavijeh, R. Scott, F. Seviaryn, R. Maev
Butt fusion (BF) is the standard method for joining polyethylene (PE) pipes during gas and water pipeline construction. The joints require simple, inexpensive and effective non-destructive testing techniques. Ultrasonic inspection is the most suitable approach; however, joint geometry requires specific configuration of the acoustic beam. In this article, a custom-designed ultrasonic chord transducer optimised for a specific pipe diameter is described. It is demonstrated how variations of sound speed and attenuation in pipe material with temperature variations affects the operation of this type of transducer. A variety of common defects, including cold fusion, dust, dirt, grass contamination, voids, etc, are simulated inside the joint and used for technique development. Analysis of an A-scan produced in pitch-catch mode allows for the evaluation of joint quality and the classification of defect type.
{"title":"Application of a chord transducer for ultrasonic detection and characterisation of defects in MDPE butt fusion joints","authors":"M. S. Alavijeh, R. Scott, F. Seviaryn, R. Maev","doi":"10.1784/insi.2022.64.10.560","DOIUrl":"https://doi.org/10.1784/insi.2022.64.10.560","url":null,"abstract":"Butt fusion (BF) is the standard method for joining polyethylene (PE) pipes during gas and water pipeline construction. The joints require simple, inexpensive and effective non-destructive testing techniques. Ultrasonic inspection is the most suitable approach; however, joint geometry\u0000 requires specific configuration of the acoustic beam. In this article, a custom-designed ultrasonic chord transducer optimised for a specific pipe diameter is described. It is demonstrated how variations of sound speed and attenuation in pipe material with temperature variations affects the\u0000 operation of this type of transducer. A variety of common defects, including cold fusion, dust, dirt, grass contamination, voids, etc, are simulated inside the joint and used for technique development. Analysis of an A-scan produced in pitch-catch mode allows for the evaluation of joint quality\u0000 and the classification of defect type.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"1 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"129322648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2022-10-01DOI: 10.1784/insi.2022.64.10.582
Tong Fu, Ping-Sen Chen, Huaqiang Liu
The surface quality of a material significantly affects the accurate measurement of the ultrasonic attenuation coefficient. In this research, a roughness correction method for experimental calculation of the attenuation coefficient using contact transducers is proposed. Firstly, the losses due to the scattering from a rough interface are analysed. According to the mechanism of ultrasonic waves reflected from a thin layer between two solid media, the frequency-dependent reflection coefficient of the coupling interface involving a roughness parameter is derived based on the phase-screen approximation theory. Then, a compensation model is established to correct the error caused by the surface roughness. 304 stainless steel and 45 steel specimens with different levels of surface roughness are prepared and ultrasonic measurements are implemented using both the through-transmission and pulse-echo methods. The experimental results show that the proposed correction method can effectively eliminate the losses caused by surface roughness and improve the measurement accuracy of the attenuation coefficient.
{"title":"Roughness correction method for the measurement of attenuation coefficient using contact transducers","authors":"Tong Fu, Ping-Sen Chen, Huaqiang Liu","doi":"10.1784/insi.2022.64.10.582","DOIUrl":"https://doi.org/10.1784/insi.2022.64.10.582","url":null,"abstract":"The surface quality of a material significantly affects the accurate measurement of the ultrasonic attenuation coefficient. In this research, a roughness correction method for experimental calculation of the attenuation coefficient using contact transducers is proposed. Firstly, the\u0000 losses due to the scattering from a rough interface are analysed. According to the mechanism of ultrasonic waves reflected from a thin layer between two solid media, the frequency-dependent reflection coefficient of the coupling interface involving a roughness parameter is derived based on\u0000 the phase-screen approximation theory. Then, a compensation model is established to correct the error caused by the surface roughness. 304 stainless steel and 45 steel specimens with different levels of surface roughness are prepared and ultrasonic measurements are implemented using both the\u0000 through-transmission and pulse-echo methods. The experimental results show that the proposed correction method can effectively eliminate the losses caused by surface roughness and improve the measurement accuracy of the attenuation coefficient.","PeriodicalId":344397,"journal":{"name":"Insight - Non-Destructive Testing and Condition Monitoring","volume":"61 1","pages":"0"},"PeriodicalIF":0.0,"publicationDate":"2022-10-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"124013520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":0,"RegionCategory":"","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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