Digital image of pipeline weld is an important basis for the reliability management of pipeline welds.However,the error rate of artificial discrimination is high.In order to increase the defect identification accuracy...Digital image of pipeline weld is an important basis for the reliability management of pipeline welds.However,the error rate of artificial discrimination is high.In order to increase the defect identification accuracy of digital image of pipeline weld,we adopted several methods(e.g.multiple edge detection,detection channel and threshold segmentation)to carry out image processing on the image defects of pipeline welds.Then,a defect characteristic database on the digital images of pipeline welds was constructed,including grayscale difference,equivalent area(S/C),circularity,entropy,correlation and other parameters.Furthermore,a multi-classifier construction(SVM)model was established.Thus,the classification and evaluation on the defects in the digital images of pipeline welds were realized.Finally,an automatic defect identification software for digital image of pipeline weld was developed and verified on site.And the following research results were obtained.First,after image processing,the edge detection results obtained by Canny and other algorithms are satisfactory when there is no noise.In the case of noise,however,pseudo-edge emerges in the detection results.In this case,the automatic threshold selection method shall be adopted to detect the image edge to obtain the rational threshold.Second,there are 14 parameters in the defect characteristic database,including shape characteristic,lamination characteristic and image length pixel.Third,by virtue of the SVM classification model,the shape characteristics of each type of defect can be clarified,and the defect characteristics can be identified,such as crack,slag inclusion,air hole,incomplete penetration,non-fusion and strip.Based on field application,the following results were obtained.First,this automatic defect identification technology is applicable to quality identification and evaluation of various defects in pipeline welds.Second,its identification accuracy is higher than 90%.Third,by virtue of this technology,automatic defect identification and evaluation of digital image of pipeline weld is realized.In conclusion,these research results help to ensure the safe operation of pipelines.展开更多
The accurate identification of material defects is critical for ensuring structural integrity and performance.Traditional computational methods often struggle to balance efficiency and physical fidelity in complex mat...The accurate identification of material defects is critical for ensuring structural integrity and performance.Traditional computational methods often struggle to balance efficiency and physical fidelity in complex material systems.This paper presents a novel approach integrating physics-informed neural networks(PINNs)with the phase field method to address these challenges.Our approach leverages a phase field variable to delineate intact regions from voids,while a stress degradation model modifies mechanical responses at defect sites.Neural networks serve as surrogate forward solvers to predict displacement and stress fields,enabling rapid simulations.To ensure compatibility with physical laws,the framework embeds governing equations into the training loss function.Additionally,a data-driven term minimizes discrepancies between simulated and experimentally measured strain fields,enhancing defect localization precision.Numerical experiments validate the framework’s robustness across diverse configurations,including circular,elliptical,irregular,and multiple voids,as well as material behaviors,extending from linear elastic to hyperelastic models.The results demonstrate superior accuracy in identifying void geometry,size,and spatial distribution compared to conventional methods.The proposed approach’s adaptability to complex geometries and material nonlinearities highlights its broad applicability in aerospace,automotive,and biomedical industries.展开更多
The contact network dropper works in a harsh environment,and suffers from the impact effect of pantographs during running of trains,which may lead to faults such as slack and broken of the dropper wire and broken of t...The contact network dropper works in a harsh environment,and suffers from the impact effect of pantographs during running of trains,which may lead to faults such as slack and broken of the dropper wire and broken of the current-carrying ring.Due to the low intelligence and poor accuracy of the dropper fault detection network,an improved fully convolutional one-stage(FCOS)object detection network was proposed to improve the detection capability of the dropper condition.Firstly,by adjusting the parameterαin the network focus loss function,the problem of positive and negative sample imbalance in the network training process was eliminated.Secondly,the generalized intersection over union(GIoU)calculation was introduced to enhance the network’s ability to recognize the relative spatial positions of the prediction box and the bounding box during the regression calculation.Finally,the improved network was used to detect the status of dropper pictures.The detection speed was 150 sheets per millisecond,and the MAP of different status detection was 0.9512.Through the simulation comparison with other object detection networks,it was proved that the improved FCOS network had advantages in both detection time and accuracy,and could identify the state of dropper accurately.展开更多
This paper introduces the identification of the defects on the fabric by using two-double neural network and wavelet analysis. The purpose is to fit for the automatic cloth inspection system and to avoid the disadvant...This paper introduces the identification of the defects on the fabric by using two-double neural network and wavelet analysis. The purpose is to fit for the automatic cloth inspection system and to avoid the disadvantages of traditional human inspection. Firstly, training the normal fabric to acquire its characteristics and then using the BP neural network to tell the normal fabric apart from the one with defects. Secondly, doing the two-dimeusional discrete wavelet transformation based on the image of the defects, then wiping off the proper characteristics of the fabric, and identifying the defects utilizing the trained BP neural network. It is proved that this method is of high speed and accuracy. It comes up to the requirement of automatic cloth inspection.展开更多
Aiming at the problems of single classification method and high classification cost of kiwifruit in China,we proposed a grading method based on kiwifruit surface defects.A set of kiwifruit image acquisition system was...Aiming at the problems of single classification method and high classification cost of kiwifruit in China,we proposed a grading method based on kiwifruit surface defects.A set of kiwifruit image acquisition system was built.The K-means clustering segmentation algorithm was used to segment the surface defects,and then color contrast was performed to determine whether it was a piece of defective fruit.Then,the shape features of normal fruit were extracted and an SVM classifier was designed to further determine its grade.This method has the advantages of low cost,simple algorithm and high efficiency,which opens a new way for fruit classification,and is of great significance to promoting the development of fruit classification industry in China and enhancing international competitiveness.展开更多
In order to solve the problems of TEV(transient earth voltage) utilization,such as single judgment criterion and low reliability of PD detection in HV switchboard,this paper discussed the method on how to make a bette...In order to solve the problems of TEV(transient earth voltage) utilization,such as single judgment criterion and low reliability of PD detection in HV switchboard,this paper discussed the method on how to make a better utilization of TEV in PD detection of metal-enclosed switchgears.Through discussing the relationship among the temporal phase,number of pulses and threshold of measuring and extracting the features corresponding to different typical defects,test results showed that the needle discharge distributed in 0°～90°and 200°～340°with low appearance probability above high measurement threshold;internal discharge distributed in 0°～90°and 270°～315°,and showed a similar decreasing trend under increasing of the threshold in the two regions;suspended discharge distributed in 0°～135°and 180°～315°where the PD in negative half periods decreased more seriously than those in positive half with increasing threshold.These results followed the doctrine of consistency with the conclusions that were obtained by using the traditional pulse current method.The possibility of using TEV method to make identification of PD defects has been proved to prepare for further research.展开更多
基金supported by the National Key R&D Program of China“R&D of security technologies and equipment for national oil and gas reserves”(No.:2017YFC0805800)National Natural Science Foundation of China“Model study of hydrogen-damage induced failure and integration evaluation for X80 pipe steel”(No.:51874322).
文摘Digital image of pipeline weld is an important basis for the reliability management of pipeline welds.However,the error rate of artificial discrimination is high.In order to increase the defect identification accuracy of digital image of pipeline weld,we adopted several methods(e.g.multiple edge detection,detection channel and threshold segmentation)to carry out image processing on the image defects of pipeline welds.Then,a defect characteristic database on the digital images of pipeline welds was constructed,including grayscale difference,equivalent area(S/C),circularity,entropy,correlation and other parameters.Furthermore,a multi-classifier construction(SVM)model was established.Thus,the classification and evaluation on the defects in the digital images of pipeline welds were realized.Finally,an automatic defect identification software for digital image of pipeline weld was developed and verified on site.And the following research results were obtained.First,after image processing,the edge detection results obtained by Canny and other algorithms are satisfactory when there is no noise.In the case of noise,however,pseudo-edge emerges in the detection results.In this case,the automatic threshold selection method shall be adopted to detect the image edge to obtain the rational threshold.Second,there are 14 parameters in the defect characteristic database,including shape characteristic,lamination characteristic and image length pixel.Third,by virtue of the SVM classification model,the shape characteristics of each type of defect can be clarified,and the defect characteristics can be identified,such as crack,slag inclusion,air hole,incomplete penetration,non-fusion and strip.Based on field application,the following results were obtained.First,this automatic defect identification technology is applicable to quality identification and evaluation of various defects in pipeline welds.Second,its identification accuracy is higher than 90%.Third,by virtue of this technology,automatic defect identification and evaluation of digital image of pipeline weld is realized.In conclusion,these research results help to ensure the safe operation of pipelines.
基金supported by the Strategic Priority Research Program of the Chinese Academy of Sciences(Grant No.XDB0620103)the Fundamental Research Funds for the Central Universities(Grant No.E2EG2202X2).
文摘The accurate identification of material defects is critical for ensuring structural integrity and performance.Traditional computational methods often struggle to balance efficiency and physical fidelity in complex material systems.This paper presents a novel approach integrating physics-informed neural networks(PINNs)with the phase field method to address these challenges.Our approach leverages a phase field variable to delineate intact regions from voids,while a stress degradation model modifies mechanical responses at defect sites.Neural networks serve as surrogate forward solvers to predict displacement and stress fields,enabling rapid simulations.To ensure compatibility with physical laws,the framework embeds governing equations into the training loss function.Additionally,a data-driven term minimizes discrepancies between simulated and experimentally measured strain fields,enhancing defect localization precision.Numerical experiments validate the framework’s robustness across diverse configurations,including circular,elliptical,irregular,and multiple voids,as well as material behaviors,extending from linear elastic to hyperelastic models.The results demonstrate superior accuracy in identifying void geometry,size,and spatial distribution compared to conventional methods.The proposed approach’s adaptability to complex geometries and material nonlinearities highlights its broad applicability in aerospace,automotive,and biomedical industries.
基金supported by Natural Science Foundation of Gansu Province(No.20JR10RA216)。
文摘The contact network dropper works in a harsh environment,and suffers from the impact effect of pantographs during running of trains,which may lead to faults such as slack and broken of the dropper wire and broken of the current-carrying ring.Due to the low intelligence and poor accuracy of the dropper fault detection network,an improved fully convolutional one-stage(FCOS)object detection network was proposed to improve the detection capability of the dropper condition.Firstly,by adjusting the parameterαin the network focus loss function,the problem of positive and negative sample imbalance in the network training process was eliminated.Secondly,the generalized intersection over union(GIoU)calculation was introduced to enhance the network’s ability to recognize the relative spatial positions of the prediction box and the bounding box during the regression calculation.Finally,the improved network was used to detect the status of dropper pictures.The detection speed was 150 sheets per millisecond,and the MAP of different status detection was 0.9512.Through the simulation comparison with other object detection networks,it was proved that the improved FCOS network had advantages in both detection time and accuracy,and could identify the state of dropper accurately.
文摘This paper introduces the identification of the defects on the fabric by using two-double neural network and wavelet analysis. The purpose is to fit for the automatic cloth inspection system and to avoid the disadvantages of traditional human inspection. Firstly, training the normal fabric to acquire its characteristics and then using the BP neural network to tell the normal fabric apart from the one with defects. Secondly, doing the two-dimeusional discrete wavelet transformation based on the image of the defects, then wiping off the proper characteristics of the fabric, and identifying the defects utilizing the trained BP neural network. It is proved that this method is of high speed and accuracy. It comes up to the requirement of automatic cloth inspection.
基金Supported by the Chinese Society of Logistics(2021CSLKT3-286)。
文摘Aiming at the problems of single classification method and high classification cost of kiwifruit in China,we proposed a grading method based on kiwifruit surface defects.A set of kiwifruit image acquisition system was built.The K-means clustering segmentation algorithm was used to segment the surface defects,and then color contrast was performed to determine whether it was a piece of defective fruit.Then,the shape features of normal fruit were extracted and an SVM classifier was designed to further determine its grade.This method has the advantages of low cost,simple algorithm and high efficiency,which opens a new way for fruit classification,and is of great significance to promoting the development of fruit classification industry in China and enhancing international competitiveness.
基金Supported by National Natural Science Foundation of China(50907051)
文摘In order to solve the problems of TEV(transient earth voltage) utilization,such as single judgment criterion and low reliability of PD detection in HV switchboard,this paper discussed the method on how to make a better utilization of TEV in PD detection of metal-enclosed switchgears.Through discussing the relationship among the temporal phase,number of pulses and threshold of measuring and extracting the features corresponding to different typical defects,test results showed that the needle discharge distributed in 0°～90°and 200°～340°with low appearance probability above high measurement threshold;internal discharge distributed in 0°～90°and 270°～315°,and showed a similar decreasing trend under increasing of the threshold in the two regions;suspended discharge distributed in 0°～135°and 180°～315°where the PD in negative half periods decreased more seriously than those in positive half with increasing threshold.These results followed the doctrine of consistency with the conclusions that were obtained by using the traditional pulse current method.The possibility of using TEV method to make identification of PD defects has been proved to prepare for further research.
