Pub Date : 2021-03-04DOI: 10.1080/09349847.2021.1883167
Haicheng Song, N. Yusa
ABSTRACT Structural health monitoring (SHM), which allows the detection of defects at an early stage by attaching sensors to the target, is an effective method of enhancing the reliability and the safety of important engineering structures. One of the practical difficulties of SHM is that usually a large area must be monitored using a limited number of sensors fixed at certain locations. And the sensor placement is a decisive contributor to the detection capability of SHM because measured signals generally depend on the location of a defect with respect to a sensor. In order to quantify the detection sensitivity more reasonably, this study proposes an analytical method based on a closed-form probability density function and a numerical method based on Monte Carlo simulation to quantify the detection sensitivity, taking into account the randomness of sensor location. The effectiveness of the proposed detection sensitivity analysis model has been examined using simulated inspection signals of low frequency electromagnetic monitoring for detecting full circumferential pipe wall thinning.
{"title":"A Detection Sensitivity Analysis Model for Structural Health Monitoring to Inspect Wall Thinning considering Random Sensor Location","authors":"Haicheng Song, N. Yusa","doi":"10.1080/09349847.2021.1883167","DOIUrl":"https://doi.org/10.1080/09349847.2021.1883167","url":null,"abstract":"ABSTRACT Structural health monitoring (SHM), which allows the detection of defects at an early stage by attaching sensors to the target, is an effective method of enhancing the reliability and the safety of important engineering structures. One of the practical difficulties of SHM is that usually a large area must be monitored using a limited number of sensors fixed at certain locations. And the sensor placement is a decisive contributor to the detection capability of SHM because measured signals generally depend on the location of a defect with respect to a sensor. In order to quantify the detection sensitivity more reasonably, this study proposes an analytical method based on a closed-form probability density function and a numerical method based on Monte Carlo simulation to quantify the detection sensitivity, taking into account the randomness of sensor location. The effectiveness of the proposed detection sensitivity analysis model has been examined using simulated inspection signals of low frequency electromagnetic monitoring for detecting full circumferential pipe wall thinning.","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"32 1","pages":"74 - 87"},"PeriodicalIF":1.4,"publicationDate":"2021-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"81815520","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-04DOI: 10.1080/09349847.2021.1887418
Fei Sha, Dongyu Xu, Xin Cheng, Shi-feng Huang
ABSTRACT An embedded smart piezoelectric sensor was fabricated, and the encapsulation material was prepared with cement, epoxy resin, curing agent, and improvement additives. Structural health monitoring (SHM) methods based on dynamic stress-sensing capability of piezoelectric sensor were presented. Mechanical Testing & Simulation (MTS) amplitude-scanning and frequency-scanning dynamic loadings were designed. Mechanical performance of encapsulation material, i.e., strength, Young modulus, and stress transmitting loss; the effects of different loading frequencies on output voltages; and stress sensitivities (V/MPa), were investigated. The electromechanical impedance and mechanical responses of embedded sensors with various loadings were studied in concrete. Theoretical formula indicates that output voltage is mainly related with external stress and area of Piezoelectric Lead Zirconate Titanate (PZT) ceramic. The optimized ratio of 4:2:0.5:1.6–4:2:0.5:2 is satisfactory and it can ensure optimal mechanical performance of encapsulation material. Stress sensitivities increase with the areas of PZT ceramic, and the effects of thickness on sensitivities are not obvious. The impedance response curve has left shifting tendency with the increase of dynamic cycles and loading values. The three-point bending destruction during concrete static loading can be in real-time reflected. The embedded sensors were suitable for dynamic mechanical monitoring in concrete. The excellent mechanical sensing performance exhibits great application potentials for SHM of concrete in civil engineering.
{"title":"Mechanical Sensing Properties of Embedded Smart Piezoelectric Sensor for Structural Health Monitoring of Concrete","authors":"Fei Sha, Dongyu Xu, Xin Cheng, Shi-feng Huang","doi":"10.1080/09349847.2021.1887418","DOIUrl":"https://doi.org/10.1080/09349847.2021.1887418","url":null,"abstract":"ABSTRACT An embedded smart piezoelectric sensor was fabricated, and the encapsulation material was prepared with cement, epoxy resin, curing agent, and improvement additives. Structural health monitoring (SHM) methods based on dynamic stress-sensing capability of piezoelectric sensor were presented. Mechanical Testing & Simulation (MTS) amplitude-scanning and frequency-scanning dynamic loadings were designed. Mechanical performance of encapsulation material, i.e., strength, Young modulus, and stress transmitting loss; the effects of different loading frequencies on output voltages; and stress sensitivities (V/MPa), were investigated. The electromechanical impedance and mechanical responses of embedded sensors with various loadings were studied in concrete. Theoretical formula indicates that output voltage is mainly related with external stress and area of Piezoelectric Lead Zirconate Titanate (PZT) ceramic. The optimized ratio of 4:2:0.5:1.6–4:2:0.5:2 is satisfactory and it can ensure optimal mechanical performance of encapsulation material. Stress sensitivities increase with the areas of PZT ceramic, and the effects of thickness on sensitivities are not obvious. The impedance response curve has left shifting tendency with the increase of dynamic cycles and loading values. The three-point bending destruction during concrete static loading can be in real-time reflected. The embedded sensors were suitable for dynamic mechanical monitoring in concrete. The excellent mechanical sensing performance exhibits great application potentials for SHM of concrete in civil engineering.","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"1 1","pages":"88 - 112"},"PeriodicalIF":1.4,"publicationDate":"2021-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"90422403","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-03-04DOI: 10.1080/09349847.2021.1877857
Saman Rashidyan, T. Ng, A. Maji
ABSTRACT Nondestructive Sonic Echo (SE) field tests have shown that this method does not have a satisfactory performance in determining the depth of piles fully buried underneath pile caps. In the current study, we endeavored to investigate the possibility of improving SE methodology to obtain interpretable results leading to determining the depth of buried piles in such foundations. The results obtained from the investigated numerical models indicated that the location of the pile toe could be determined when the height of the pile cap is less than 1 m. However, this value is questionable since it has been concluded in the absence of surrounding soil damping. In real bridge foundations with surrounding soils, the SE method may only be able to detect the length of a pile located beneath a cap with a height significantly smaller than 1 m. In summary, our simplified models show that the SE test is not a proper method to determine the length of fully buried piles supporting caps due to the limitations and difficulties discussed in the article.
{"title":"Limitations of Sonic Echo Testing on Buried Piles of Unknown Bridge Foundations","authors":"Saman Rashidyan, T. Ng, A. Maji","doi":"10.1080/09349847.2021.1877857","DOIUrl":"https://doi.org/10.1080/09349847.2021.1877857","url":null,"abstract":"ABSTRACT Nondestructive Sonic Echo (SE) field tests have shown that this method does not have a satisfactory performance in determining the depth of piles fully buried underneath pile caps. In the current study, we endeavored to investigate the possibility of improving SE methodology to obtain interpretable results leading to determining the depth of buried piles in such foundations. The results obtained from the investigated numerical models indicated that the location of the pile toe could be determined when the height of the pile cap is less than 1 m. However, this value is questionable since it has been concluded in the absence of surrounding soil damping. In real bridge foundations with surrounding soils, the SE method may only be able to detect the length of a pile located beneath a cap with a height significantly smaller than 1 m. In summary, our simplified models show that the SE test is not a proper method to determine the length of fully buried piles supporting caps due to the limitations and difficulties discussed in the article.","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"71 1","pages":"59 - 73"},"PeriodicalIF":1.4,"publicationDate":"2021-03-04","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"74051648","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2021-01-02DOI: 10.1080/09349847.2020.1868639
A. V, S. Thirunavukkarasu, Anish Kumar
ABSTRACT In this study, the discrete wavelet transform (DWT)-based signal processing methodology is applied for eliminating noise due to permeability variations in saturation eddy current (EC) testing signals from nickel tubes. The nickel tubes are of 0.3 mm thickness and 6.6 mm outer diameter. Systematic studies have been carried out to optimize the wavelet functions, number of decomposition levels, and thresholding algorithm for DWT processing based on the signal-to-noise ratio (SNR). The DWT processing has enabled reliable detection of a 0.1 mm deep notch located on the inner surface of the tubes meeting its stringent quality requirements. Application of signal processing-based methodology has resulted in an improvement in SNR of 11.4 dB as against 5.1 dB for the raw signals.
{"title":"Quality Assurance of Thin-Walled Nickel Tubes by Eddy Current (EC) Testing Using the Discrete Wavelet Transform (DWT) Processing Methodology","authors":"A. V, S. Thirunavukkarasu, Anish Kumar","doi":"10.1080/09349847.2020.1868639","DOIUrl":"https://doi.org/10.1080/09349847.2020.1868639","url":null,"abstract":"ABSTRACT In this study, the discrete wavelet transform (DWT)-based signal processing methodology is applied for eliminating noise due to permeability variations in saturation eddy current (EC) testing signals from nickel tubes. The nickel tubes are of 0.3 mm thickness and 6.6 mm outer diameter. Systematic studies have been carried out to optimize the wavelet functions, number of decomposition levels, and thresholding algorithm for DWT processing based on the signal-to-noise ratio (SNR). The DWT processing has enabled reliable detection of a 0.1 mm deep notch located on the inner surface of the tubes meeting its stringent quality requirements. Application of signal processing-based methodology has resulted in an improvement in SNR of 11.4 dB as against 5.1 dB for the raw signals.","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"94 1","pages":"24 - 37"},"PeriodicalIF":1.4,"publicationDate":"2021-01-02","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75616745","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-01DOI: 10.1080/09349847.2020.1841862
B. Valeske, Ahmad Osman, Florian Römer, R. Tschuncky
ABSTRACT Industry 4.0 (I4.0) describes the current revolution of the industrial world with a strong impact on the complete production sector. Data about production processes and the corresponding material and product status are the key elements. All over the world, the protagonists of I4.0 are facing the challenges to define appropriate concepts for I4.0 infrastructure, data exchange, communication interfaces and efficient procedures for the interaction of I4.0 elements. The role of future Nondestructive Evaluation (NDE4.0) and corresponding workflows (i.e. data generation and evaluation) will change accordingly. Thus, NDE4.0 systems will be elements of the Industrial Internet of Things (IIoT) that communicate with the production machines and devices. They become an integral part of the digital production world and the industrial data space. This paper is a summarized overview of our current developments as well as of general key technologies and future challenges to enable the paradigm change from classical NDT toward NDE4.0, starting with approaches on signal processing, artificial intelligence-based information generation and decision making, generic data formats and communication protocols. For illustration purposes, prototypical implementations of our work are presented. This includes a pilot development of a modern human- machine-interaction by the use of assistance technologies for manual inspection.
{"title":"Next Generation NDE Sensor Systems as IIoT Elements of Industry 4.0","authors":"B. Valeske, Ahmad Osman, Florian Römer, R. Tschuncky","doi":"10.1080/09349847.2020.1841862","DOIUrl":"https://doi.org/10.1080/09349847.2020.1841862","url":null,"abstract":"ABSTRACT Industry 4.0 (I4.0) describes the current revolution of the industrial world with a strong impact on the complete production sector. Data about production processes and the corresponding material and product status are the key elements. All over the world, the protagonists of I4.0 are facing the challenges to define appropriate concepts for I4.0 infrastructure, data exchange, communication interfaces and efficient procedures for the interaction of I4.0 elements. The role of future Nondestructive Evaluation (NDE4.0) and corresponding workflows (i.e. data generation and evaluation) will change accordingly. Thus, NDE4.0 systems will be elements of the Industrial Internet of Things (IIoT) that communicate with the production machines and devices. They become an integral part of the digital production world and the industrial data space. This paper is a summarized overview of our current developments as well as of general key technologies and future challenges to enable the paradigm change from classical NDT toward NDE4.0, starting with approaches on signal processing, artificial intelligence-based information generation and decision making, generic data formats and communication protocols. For illustration purposes, prototypical implementations of our work are presented. This includes a pilot development of a modern human- machine-interaction by the use of assistance technologies for manual inspection.","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"113 1","pages":"340 - 369"},"PeriodicalIF":1.4,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86225820","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-01DOI: 10.1080/09349847.2020.1841863
K. Gogolinskiy, V. Syasko
ABSTRACT In the article urgent tasks of the development of NDT and CM metrological assurance, as well as problems of standardization of general principles and specific technical solutions in the context of the fourth industrial revolution main trends are discussed. The following questions are considered: Development of the NDT metrological assurance based on the concept of multi-parameter measurements, development of standards for remote adjustment and calibration of intelligent sensors in distributed measuring networks. Attestation and verification issues (metrological assurance) of digital models (twins) for inspected objects and measuring and testing devices – Methodological principles for constructing self-monitoring and self-calibrating intelligent measuring transducers (sensors) for cyber-physical systems of smart manufacturing and distributed condition monitoring systems (quality infrastructure). - Development of standards for various components of distributed CM systems (smart sensors interfaces and protocols for transmitting information, software, and hardware platforms for collecting and processing information, digital twins of tools and control objects) embedded in the overall standardization system for smart industries, which realized key principles of Industry 4.0 in terms of compatibility, transparency, technical support, and decentralization of management decisions based on intelligence machine algorithms.
{"title":"Metrological Assurance and Standardization of Advanced Tools and Technologies for nondestructive Testing and Condition Monitoring (NDT4.0)","authors":"K. Gogolinskiy, V. Syasko","doi":"10.1080/09349847.2020.1841863","DOIUrl":"https://doi.org/10.1080/09349847.2020.1841863","url":null,"abstract":"ABSTRACT In the article urgent tasks of the development of NDT and CM metrological assurance, as well as problems of standardization of general principles and specific technical solutions in the context of the fourth industrial revolution main trends are discussed. The following questions are considered: Development of the NDT metrological assurance based on the concept of multi-parameter measurements, development of standards for remote adjustment and calibration of intelligent sensors in distributed measuring networks. Attestation and verification issues (metrological assurance) of digital models (twins) for inspected objects and measuring and testing devices – Methodological principles for constructing self-monitoring and self-calibrating intelligent measuring transducers (sensors) for cyber-physical systems of smart manufacturing and distributed condition monitoring systems (quality infrastructure). - Development of standards for various components of distributed CM systems (smart sensors interfaces and protocols for transmitting information, software, and hardware platforms for collecting and processing information, digital twins of tools and control objects) embedded in the overall standardization system for smart industries, which realized key principles of Industry 4.0 in terms of compatibility, transparency, technical support, and decentralization of management decisions based on intelligence machine algorithms.","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"23 1","pages":"325 - 339"},"PeriodicalIF":1.4,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"86983374","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-01DOI: 10.1080/09349847.2020.1841864
Maryam Shafiei Alavijeh, R. Scott, F. Seviaryn, R. Maev
ABSTRACT Pipe joints mostly form the weakest points in pipeline networks. In-field joints are prone to various flaws. Thus, the infrastructure industry requires an effective inspection technique. Our work focused on evaluating the performance of chord-type transducers for flaw detection in polyethylene (PE) pipe joints. Various artificially introduced flaws were fabricated and tested for statistical estimation of system performance. A-scans data was gathered to develop and assess the viability of a deep learning approach for automated flaw detection. Such an automated “smart” quality control method aligns with requirements of an nondestructive evaluation (NDE) 4.0 platform which can be utilized to achieve reliable and real-time inspection. In this we will introduce results of our current development, starting with approaches to generic data formats, communication protocols, signal processing, artificial intelligence-based (AI) information generation, and decision making. For each of the aspects, results and prototypical implementations will be provided. This includes a pilot development for modern human-machine-interaction using assistive technologies for manual NDE 4.0 inspection. This gives an outlook on further challenges and possible approaches for requirements in the context of secure data exchange, trusted and reliable AI processing, new standardization procedures, and validation of new “smart” NDE 4.0 ultrasonic inspection systems.
{"title":"NDE 4.0 compatible ultrasound inspection of butt-fused joints of medium-density polyethylene gas pipes, using chord-type transducers supported by customized deep learning models","authors":"Maryam Shafiei Alavijeh, R. Scott, F. Seviaryn, R. Maev","doi":"10.1080/09349847.2020.1841864","DOIUrl":"https://doi.org/10.1080/09349847.2020.1841864","url":null,"abstract":"ABSTRACT Pipe joints mostly form the weakest points in pipeline networks. In-field joints are prone to various flaws. Thus, the infrastructure industry requires an effective inspection technique. Our work focused on evaluating the performance of chord-type transducers for flaw detection in polyethylene (PE) pipe joints. Various artificially introduced flaws were fabricated and tested for statistical estimation of system performance. A-scans data was gathered to develop and assess the viability of a deep learning approach for automated flaw detection. Such an automated “smart” quality control method aligns with requirements of an nondestructive evaluation (NDE) 4.0 platform which can be utilized to achieve reliable and real-time inspection. In this we will introduce results of our current development, starting with approaches to generic data formats, communication protocols, signal processing, artificial intelligence-based (AI) information generation, and decision making. For each of the aspects, results and prototypical implementations will be provided. This includes a pilot development for modern human-machine-interaction using assistive technologies for manual NDE 4.0 inspection. This gives an outlook on further challenges and possible approaches for requirements in the context of secure data exchange, trusted and reliable AI processing, new standardization procedures, and validation of new “smart” NDE 4.0 ultrasonic inspection systems.","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"51 1","pages":"290 - 305"},"PeriodicalIF":1.4,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75955817","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-11-01DOI: 10.1080/09349847.2020.1847374
J. Aldrin, J. Wertz, J. Welter, E. Lindgren, N. Schehl, V. Kramb, David Zainey
ABSTRACT This study explores the application of algorithms with linear array ultrasonic testing for the characterization of hidden regions of impact damage in composites. An idealized ray tracing model was used to demonstrate the sensitivity of transmitted signals to the hidden impact profile, and a numerical model was used to provide insight on the incident field generated by linear array elements. Experimental studies were performed highlighting the differences in the response from no flaw, columnar and trapezoidal profiles. Algorithms were implemented to process full matrix capture data, register pitch-catch signals with the top delamination location and extent, and improve the signal-to-noise through combining multiple pitch-catch acquisitions. Lastly, a classifier was developed and verification testing demonstrated the ability to distinguish four different hidden profiles, indicating the importance of signal registration for successful classification.
{"title":"Pitch-Catch Ultrasonic Array Characterization of the Hidden Region of Impact Damage in Composites","authors":"J. Aldrin, J. Wertz, J. Welter, E. Lindgren, N. Schehl, V. Kramb, David Zainey","doi":"10.1080/09349847.2020.1847374","DOIUrl":"https://doi.org/10.1080/09349847.2020.1847374","url":null,"abstract":"ABSTRACT This study explores the application of algorithms with linear array ultrasonic testing for the characterization of hidden regions of impact damage in composites. An idealized ray tracing model was used to demonstrate the sensitivity of transmitted signals to the hidden impact profile, and a numerical model was used to provide insight on the incident field generated by linear array elements. Experimental studies were performed highlighting the differences in the response from no flaw, columnar and trapezoidal profiles. Algorithms were implemented to process full matrix capture data, register pitch-catch signals with the top delamination location and extent, and improve the signal-to-noise through combining multiple pitch-catch acquisitions. Lastly, a classifier was developed and verification testing demonstrated the ability to distinguish four different hidden profiles, indicating the importance of signal registration for successful classification.","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"17 1","pages":"275 - 289"},"PeriodicalIF":1.4,"publicationDate":"2020-11-01","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"75166639","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
Pub Date : 2020-10-23DOI: 10.1080/09349847.2020.1836293
M. Mirzapour, E. Yahaghi, A. Movafeghi
ABSTRACT Industrial radiography is considered as one of the most important nondestructive testing methods for different inspections. The radiography images often have a poor signal-to-noise ratio mainly because of the scattered X-rays. Image processing methods may be used to enhance the contrast of radiographs for better defect detection. In this study, outcomes from three total variations (TV) based methods were analyzed and compared. Implemented algorithms were ROF-TV, non-convex p-norm total variation (NCP-TV) and non-convex logarithm-based total variation (NCLog-TV). These TV-based methods have been implemented indirectly as high pass edge-enhancing filters. Based on qualitative operator perception results, the study has shown that the application of all three methods resulted in improved image contrast enabling enhanced image detail visualization. Subtle performance differences between the outputs from different algorithms were noted, however, especially around the edges of image features. Furthermore, it was found that all implemented algorithms have similarities in performance, generate approximately the same results and are suitable for weld inspection.
{"title":"The Performance of Three Total Variation Based Algorithms for Enhancing the Contrast of Industrial Radiography Images","authors":"M. Mirzapour, E. Yahaghi, A. Movafeghi","doi":"10.1080/09349847.2020.1836293","DOIUrl":"https://doi.org/10.1080/09349847.2020.1836293","url":null,"abstract":"ABSTRACT Industrial radiography is considered as one of the most important nondestructive testing methods for different inspections. The radiography images often have a poor signal-to-noise ratio mainly because of the scattered X-rays. Image processing methods may be used to enhance the contrast of radiographs for better defect detection. In this study, outcomes from three total variations (TV) based methods were analyzed and compared. Implemented algorithms were ROF-TV, non-convex p-norm total variation (NCP-TV) and non-convex logarithm-based total variation (NCLog-TV). These TV-based methods have been implemented indirectly as high pass edge-enhancing filters. Based on qualitative operator perception results, the study has shown that the application of all three methods resulted in improved image contrast enabling enhanced image detail visualization. Subtle performance differences between the outputs from different algorithms were noted, however, especially around the edges of image features. Furthermore, it was found that all implemented algorithms have similarities in performance, generate approximately the same results and are suitable for weld inspection.","PeriodicalId":54493,"journal":{"name":"Research in Nondestructive Evaluation","volume":"14 1","pages":"10 - 23"},"PeriodicalIF":1.4,"publicationDate":"2020-10-23","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"","citationCount":null,"resultStr":null,"platform":"Semanticscholar","paperid":"87179537","PeriodicalName":null,"FirstCategoryId":null,"ListUrlMain":null,"RegionNum":4,"RegionCategory":"材料科学","ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":"","EPubDate":null,"PubModel":null,"JCR":null,"JCRName":null,"Score":null,"Total":0}
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