Insight - Non-Destructive Testing and Condition Monitoring最新文献_第8页

Study on surface crack detection of ferromagnetic materials based on remanence 基于剩余物的铁磁材料表面裂纹检测研究

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2022-12-01 DOI: 10.1784/insi.2022.64.12.688

Jiarui Feng, Entao Yao, Ping Wang, Yuxia Shi

{"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}

引用次数: 0

Welding defect detection in nuclear power plant spent fuel pool panels based on alternating current field measurement: experimental and finite element analysis 基于交流电场测量的核电厂乏燃料池板焊接缺陷检测:实验与有限元分析

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2022-12-01 DOI: 10.1784/insi.2022.64.12.695

Zhaoming Zhou, Chunfu Yang, Liyan Liu, Donghong Zhao, Kai Li

{"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}

引用次数: 0

In-situ fatigue damage monitoring of rolled Al-Zn-Mg alloy using an advanced acoustic emission technique 基于声发射技术的轧制Al-Zn-Mg合金疲劳损伤原位监测

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2022-12-01 DOI: 10.1784/insi.2022.64.12.702

Ronggui Zhu, Dandan Chi, X. Zhan

引用次数: 0

Metal-loss defect depth inversion in oil and gas pipelines based on Bayesian regularisation neural network 基于贝叶斯正则化神经网络的油气管道金属损失缺陷深度反演

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2022-12-01 DOI: 10.1784/insi.2022.64.12.680

Fengmiao Tu, Min Wei, Jun Liu, Lixia Jiang, Jia Zhang

{"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}

引用次数: 0

Development of digital tools to enable remote ultrasonic inspection of fusion ractor in-vessel components 数字工具的开发，使远程超声检查核聚变反应堆的容器内组件

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2022-11-01 DOI: 10.1784/insi.2022.64.11.633

R. Sanderson, A. Sanderson, K. Akowua, H. Livesey

{"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}

引用次数: 1

A prediction model for oil and gas pipeline deformation based on ACM inspection signal waveforms 基于ACM检测信号波形的油气管道变形预测模型

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2022-10-01 DOI: 10.1784/insi.2022.64.10.573

Jiaxing Xin, Jinzhong Chen, Xiaolong Li, R. He, Hongwu Zhu

{"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}

引用次数: 0

Application of empirical mode decomposition to determine pile lengths subject to lateral impact 应用经验模态分解来确定受横向冲击的桩长

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2022-10-01 DOI: 10.1784/insi.2022.64.10.589

T. Nguyễn, Helsin Wang, Chung-Yue Wang

引用次数: 0

Application of a convolutional neural network in wire rope magnetic memory testing 卷积神经网络在钢丝绳磁记忆检测中的应用

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2022-10-01 DOI: 10.1784/insi.2022.64.10.566

Juwei Zhang, Bing Li, Zengguang Zhang, Qihang Chen

引用次数: 1

Application of a chord transducer for ultrasonic detection and characterisation of defects in MDPE butt fusion joints 弦式换能器在MDPE对接融合接头缺陷超声检测与表征中的应用

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2022-10-01 DOI: 10.1784/insi.2022.64.10.560

M. S. Alavijeh, R. Scott, F. Seviaryn, R. Maev

引用次数: 0

Roughness correction method for the measurement of attenuation coefficient using contact transducers 使用接触式传感器测量衰减系数的粗糙度校正方法

Insight - Non-Destructive Testing and Condition Monitoring Pub Date : 2022-10-01 DOI: 10.1784/insi.2022.64.10.582

Tong Fu, Ping-Sen Chen, Huaqiang Liu

{"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}

引用次数: 0