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defect detection Model Using Time Series Data Augmentation and Transformation

Computers, Materials & Continua 2024年第2期78卷 1713-1730页

作者： Gyu-Il Kim Hyun Yoo Han-Jin Cho Kyungyong Chung Department of Computer Science Kyonggi UniversitySuwon-si16227Korea Contents Convergence Software Research Institue Kyonggi UniversitySuwon-si16227Korea Department of Energy IT Engineering Far East UniversityEumsung-gun27601Korea Division of AI Computer Science and Engineering Kyonggi UniversitySuwon-si16227Korea

Time-series data provide important information in many fields,and their processing and analysis have been the focus of much ***,detecting anomalies is very difficult due to data imbalance,temporal dependence,and ***,methodologies for data augmentation and conversion of time series data into images for analysis have been *** paper proposes a fault detection model that uses time series data augmentation and transformation to address the problems of data imbalance,temporal dependence,and robustness to *** method of data augmentation is set as the addition of *** involves adding Gaussian noise,with the noise level set to 0.002,to maximize the generalization performance of the *** addition,we use the Markov Transition Field(MTF)method to effectively visualize the dynamic transitions of the data while converting the time series data into *** enables the identification of patterns in time series data and assists in capturing the sequential dependencies of the *** anomaly detection,the PatchCore model is applied to show excellent performance,and the detected anomaly areas are represented as heat *** allows for the detection of anomalies,and by applying an anomaly map to the original image,it is possible to capture the areas where anomalies *** performance evaluation shows that both F1-score and Accuracy are high when time series data is converted to ***,when processed as images rather than as time series data,there was a significant reduction in both the size of the data and the training *** proposed method can provide an important springboard for research in the field of anomaly detection using time series ***,it helps solve problems such as analyzing complex patterns in data lightweight.

关键词： defect detection time series deep learning data augmentation data transformation

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defect detection and repair algorithm for structures generated by topology optimization based on 3D hierarchical fully convolutional network

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Biomimetic Intelligence & Robotics 2024年第2期4卷 24-31页

作者： Zhiyu Wan Hai Lan Sichao Lin Houde Dai School of Advanced Manufacturing Fuzhou UniversityJinjiang 362251China Quanzhou Institute of Equipment Manufacturing Haixi InstitutesChinese Academy of SciencesJinjiang 362216China Fujian Key Laboratory of Special Intelligent Equipment Safety Measurement and Control Fujian Special Equipment Inspection and Research InstituteFuzhou 350008China

Customized 3D-printed structural parts are widely used in surgical *** satisfy the mechanical properties and kinematic functions of these structural parts,a topology optimization technique is adopted to obtain the optimal structural layout while meeting the constraints and ***,topology optimization currently faces some practical challenges that must be addressed,such as ensuring that structures do not have significant defects when converted to additive manufacturing *** address this problem,we designed a 3D hierarchical fully convolutional network(FCN)to predict the precise position of the defective *** on the prediction results,an effective repair strategy is adopted to repair the defective structure.A series of experiments is conducted to demonstrate the effectiveness of our *** to the 2D fully convolutional network and the rule-based detection method,our approach can accurately capture most defect structures and achieve 89.88%precision and 95.59%***,we investigate the impact of different ways to increase the receptive field of our model,as well as the trade-off between different defect-repairing *** results of the experiment demonstrate that the hierarchical structure,which increases the receptive field,can substantially improve the defect detection *** the best of our knowledge,this paper is the first to investigate 3D defect prediction and repair for topology optimization in conjunction with deep learning algorithms,providing practical tools and new perspectives for the subsequent development of topology optimization techniques.

关键词： Topology optimization Additive manufacturing Deep learning 3D semantic segmentation defect detection

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defect detection of gear parts in virtual manufacturing

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Visual Computing for Industry,Biomedicine,and Art 2023年第1期6卷 76-87页

作者： Zhenxing Xu Aizeng Wang Fei Hou Gang Zhao School of Mechanical Engineering&Automation Beihang UniversityBeijing 100191China State Key Laboratory of Computer Science Institute of SoftwareChinese Academy of SciencesBeijing 100190China University of Chinese Academy of Sciences Beijing 100049China

Gears play an important role in virtual manufacturing systems for digital twins;however,the image of gear tooth defects is difficult to acquire owing to its non-convex *** this study,a deep learning network is proposed to detect gear defects based on their point cloud *** approach mainly consists of three steps:(1)Various types of gear defects are classified into four cases(fracture,pitting,glue,and wear);A 3D gear dataset was constructed with 10000 instances following the aforementioned classification.(2)Gear-PCNet++introduces a novel Combinational Convolution Block,proposed based on the gear dataset for gear defect detection to effectively extract the local gear information and identify its complex topology;(3)Compared with other methods,experiments show that this method can achieve better recognition results for gear defects with higher efficiency and practicability.

关键词： defect detection Gear surface Gear dataset Combinational Convolution Block

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defect detection in brazed weldment with lattice structure using ultrasonic Lamb wave technique

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China Welding 2010年第1期19卷 6-10页

作者：迟大钊刚铁刘磊袁媛 State Key Laboratory of Advanced Welding Production Technology Harbin Institute of Technology Harbin 150001

In this paper, defects detection in brazed weldment with lattice structure is studied using ultrasonic Lamb wave. Based on the ultrasonic guided wave theory, the frequency dispersion curves for phase and group velocity of I Cr18Ni9Ti are obtained by solving the Rayleigh-Lamb equation. The incident angles of different modes are determined through theoretical calculation and experimental analyses. Artificial defects of through-wall slots with different dimensions are made and tested. Experimental scattering effects of the fundamental symmetric mode S2 and asymmetric modes A1 and A0 are analysed and compared. The results show that mode Ao is suitable for detecting artificial defect, and the amplitude of the received signals are in good agreement with the defect size. Brazed weldment specimen containing lack of brazing with certain dimensions is made. Using the same methodology, scattering effects produced by weld defects are measured. The results show that the clutter wave brought about by the filler metal will certainly disturbs the identification of defect signal. But, when the defect is 3.0 mm in width, the presented mode Ao could be used potentially.

关键词： ultrasonic Lamb wave defect detection brazed weldment

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defect detection in brazed weldment with lattice structure using acoustic shadow technique

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China Welding 2011年第3期20卷 63-66页

作者：迟大钊刚铁刘强 State Key Laboratory of Advanced Welding Production Technology Harbin Institute of Technology Harbin 150001

Brazed weldment with lattice structure has been widely used in aerospace industry. The non-destructive testing is often difficult because of the poor inspection accessibility. The present paper illustrates how the plane-like defect lack of brazing can be detected rapidly in this kind of structure by using ultrasonic Lamb wave. Experimental weldments are prepared and weld defect are tested using S2 mode Lamb wave. Acoustic shadow technique is employed based on Lamb wave testing method. The character of the tested D-scan image and A-scan signal is studied. The experimental results show that acoustic shadow based Lamb wave testing method is effective in detecting through-wall lack of brazing. Meanwhile, the D-scan tested data can be rapidly collected and easily interpreted compared with pulse echo bused Lamb wave testing method.

关键词： acoustic shadow ultrasonic Lamb wave defect detection brazed weldment

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defect detection and Recognition of Bare PCB Based on Computer Vision 36

Defect Detection and Recognition of Bare PCB Based on Comput...

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第36届中国控制会议

作者： Jianjie Ma School of Automation Science and Electrical Engineering Beihang University

ISBN: (纸本)9781538629185

defect detection and recognition of bare PCB plays a significant role in computer vision *** accurate and efficient approach is implemented in this *** approach is based on the comparison between the standard PCB image and the target *** images of the qualified PCBs are acquired at the same *** take an average of the images and consider it to be an initial standard *** paper suggests the concept of dynamic updation which means we update the standard image during the detection *** bilateral filtering method is used in the preprocessing *** the target image is compared with the standard image to get the difference *** a suitable threshold is obtained by analyzing the histogram of the difference image to distinguish potential defect *** the boundary-length-range method,the authenticity of each potential defect region is preliminarily *** that,we can identify the bare PCBs which have no ***,an improved region growing method is proposed to obtain the complete ranges of the real defect ***,a simple but effective method is presented to recognize the type of the *** results show that the proposed method in this paper works well.

关键词： Computer Vision defect detection Image Processing defect Recognition

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Multivariate Image Analysis in Gaussian Multi-Scale Space for defect detection

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Journal of Bionic Engineering 2009年第3期6卷 298-305页

作者： Dong-tai Liang~1 Wei-yan Deng~2 Xuan-yin Wang~1 Yang Zhang~11.State Key Laboratory of Fluid Power Transmission and Control Zhejiang University,Hangzhou 310027,P.R.China2.College of Mechanical and Electrical Engineering,China Jiliang University,Hangzhou 310018,P.R.China [a]State Key Laboratory of Fluid Power Transmission and Control Zhejiang University Hangzhou 310027 P. R. China [b]College of Mechanical and Electrical Engineering China Jiliang University Hangzhou 310018 P. R. China

Inspired by the coarse-to-fine visual perception process of human vision system,a new approach based on Gaussian multi-scale space for defect detection of industrial products was *** selecting different scale parameters of the Gaussian kernel,the multi-scale representation of the original image data could be obtained and used to constitute the multi- variate image,in which each channel could represent a perceptual observation of the original image from different *** Multivariate Image Analysis (MIA) techniques were used to extract defect features *** MIA combined Principal Component Analysis (PCA) to obtain the principal component scores of the multivariate test *** Q-statistic image, derived from the residuals after the extraction of the first principal component score and noise,could be used to efficiently reveal the surface defects with an appropriate threshold value decided by training *** results show that the proposed method performs better than the gray histogram-based *** has less sensitivity to the inhomogeneous of illumination,and has more robustness and reliability of defect detection with lower pseudo reject rate.

关键词： defect detection scale-space Gausslan multi-scale representahon principal component analysis multivariate image anaIysis

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PSP-Finder:A defect detection Method Based on Mining Correlations from Function Call Paths

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Chinese Journal of Electronics 2018年第4期27卷 776-782页

作者： CUI Zhanqi CHEN Xiang MU Yongmin PAN Minxue WANG Rongcun Computer School Beijing Information Science and Technology University Sate Key Laboratory for Novel Software Technology Nanjing University School of Computer Science and Technology Nantong University School of Computer Science and Technology China University of Mining and Technology

Large scale programs usually imply many programming rules, which are missing from the specification documents. However, if programmers violate these rules in the process of programming, it is possible to introduce software defects. Previous works on mining function call correlation patterns only use structural information of the program, while control flow, data flow or other semantic information of the program are not exploited in those approaches. As a result, the defect detecting ability is restricted and high false rate is caused. This paper proposes a defect detection method based on mining function call association rules from program paths, which can be provided by simple static analysis. Then, the programs are automatically checked against the function call association rules for detecting suspicious defects. Based on this approach,experiments are carried out on a group of open source projects. The experiment results show that this approach can improve the capability of detecting defects and find more bugs related to program execution path. In addition,the false positive function call patterns and the overhead for manually validating suspicious defects are reduced.

关键词： Function call graphs Function call paths Mining association rules defect detection

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defect detection Algorithm of Patterned Fabrics Based on Convolutional Neural Network

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Journal of Donghua University(English Edition) 2021年第1期38卷 36-42页

作者： XU Yang FEI Libin YU Zhiqi SHENG Xiaowei College of Mechanical Engineering Donghua UniversityShanghai201620China

The background pattern of patterned fabrics is complex,which has a great interference in the extraction of defect *** machine vision algorithms rely on artificially designed features,which are greatly affected by background patterns and are difficult to effectively extract flaw ***,a convolutional neural network(CNN)with automatic feature extraction is *** the basis of the two-stage detection model Faster R-CNN,Resnet-50 is used as the backbone network,and the problem of flaws with extreme aspect ratio is solved by improving the initialization algorithm of the prior frame aspect ratio,and the improved multi-scale model is designed to improve detection of small *** cascade R-CNN is introduced to improve the accuracy of defect detection,and the online hard example mining(OHEM)algorithm is used to strengthen the learning of hard samples to reduce the interference of complex backgrounds on the defect detection of patterned fabrics,and construct the focal loss as a loss function to reduce the impact of sample *** order to verify the effectiveness of the improved algorithm,a defect detection comparison experiment was set *** experimental results show that the accuracy of the defect detection algorithm of patterned fabrics in this paper can reach 95.7%,and it can accurately locate the defect location and meet the actual needs of the factory.

关键词： patterned fabrics defect detection convolutional neural network(CNN) multi-scale model cascade network

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defect detection method based on 2D entropy image segmentation

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China Welding 2020年第1期29卷 45-49页

作者： Chi Dazhao Gang Tie State Key Laboratory of Advanced Welding and Joining Harbin Institute of TechnologyHarbin 150001China

In order to improve the work efficiency of non-destructive testing(NDT)and the reliability of NDT results,an automatic method to detect defects in the ultrasonic image was *** to the characterization of ultrasonic D-scan image,clutter wave suppression and de-noising were presented ***,the image is processed by binaryzation using KSW 2 D entropy based on image segmentation *** results showed that,the global threshold based segmentation method was somewhat ineffective for D-scan image because of ***,when the image is big in size,small targets which are composed by a small amount of pixels are often ***,local threshold based image segmentation method is effective in recognizing small defects because it takes local image character into account.

关键词： ultrasonic testing defect detection 2D entropy image segmentation

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