Please wait a minute...
浙江大学学报(工学版)
Journal of ZheJiang University (Engineering Science)  2023, Vol. 57 Issue (5): 1009-1020    DOI: 10.3785/j.issn.1008-973X.2023.05.017
    
Surface defect detection method for bearing drum-shaped rollers based on fusion transformation of defective area
Qing-lin AI(),Jing-rui CUI,Bing-hai LV,Tong TONG
Key Laboratory of Special Purpose Equipment and Advanced Manufacturing Technology, Ministry of Education and Zhejiang Province, Zhejiang University of Technology, Hangzhou 310023, China
Download: HTML     PDF(2975KB) HTML
Export: BibTeX | EndNote (RIS)      

Abstract  

A surface defect detection method of bearing drum-shaped roller was proposed based on multi-sample defective area fusion data augmentation to aim at the problems of defective parts scarcity, low detection accuracy and low precision in process of drum-shaped roller surface defect detection. A drum-shaped roller surface defect sample collection system was built. The side images of the rollers were collected and used as research object. The core detection area was cropped according to distribution characteristics of the outer corner of roller in the sample image. Based on the clustering pre-selection box and the defect assessment function, the key defective area with appropriate size and aspect ratio was selected from the negative samples. The different affine transformations were performed on the selected area.The area was merged with randomly sampled positive samples to generate new data. The processed image was input into the inspection neural network for detection and classification. The experiments showed that accuracy rate and precision rate of the data-enhanced drum roller surface defect detection model were 95.2% and 97.7% respectively. After the threshold was adjusted, the final precision rate was 99.8%. The detection accuracy and precision improved effectively by integrating the complex defects characteristics of the different samples. The actual production requirements of drum roller surface defect detection can be met.

Key words： drum-shaped roller      surface defect detection      data augmentation      image classification      feature fusion     
Received: 16 December 2021      Published: 09 May 2023
CLC:  TP 399  
Fund:  国家自然科学基金资助项目（52075488）；浙江省自然科学基金资助项目（LY20E050023）
Service
E-mail this article
Add to my bookshelf
Add to citation manager
E-mail Alert
RSS
Articles by authors
Qing-lin AI
Jing-rui CUI
Bing-hai LV
Tong TONG
Cite this article:

Qing-lin AI,Jing-rui CUI,Bing-hai LV,Tong TONG. Surface defect detection method for bearing drum-shaped rollers based on fusion transformation of defective area. Journal of ZheJiang University (Engineering Science), 2023, 57(5): 1009-1020.

URL:

https://www.zjujournals.com/eng/10.3785/j.issn.1008-973X.2023.05.017     OR     https://www.zjujournals.com/eng/Y2023/V57/I5/1009


基于损伤区域融合变换的轴承鼓形滚子表面损伤检测方法

针对鼓形滚子表面损伤检测过程中损伤零件数量少、检测准确率及查准率不高的问题，提出一种基于多样本关键损伤区域融合数据增强的轴承鼓形滚子表面损伤检测方法. 搭建鼓形滚子表面损伤特征样本检测装置，采集滚子的工作面侧面图像并将侧面图像作为研究对象，根据样本图像中滚子外圆角分布特点对样本核心检测区域进行裁剪. 基于聚类预选框及损伤评估函数，从数据集负样本中筛选合适大小与纵横比的关键损伤区域，对所选区域进行不同的仿射变换并与随机采样的正样本进行融合以生成新的增强样本数据. 将经过数据增强处理的图像，输入到检测神经网络中进行检测分类. 实验表明，采用数据增强的鼓形滚子表面损伤检测模型在测试集上的准确率为95.2%，查准率为97.7%，经过正样本阈值调整后模型最终的查准率为99.8%. 该方法融合不同样本的复杂损伤特征，有效地提升了检测准确率以及查准率，满足鼓形滚子表面损伤检测的实际生产需求.


关键词： 鼓形滚子,  表面损伤检测,  数据增强,  图像分类,  特征融合 
Fig.1 Collection device for roller surface defect image sample
Fig.2 Bearing roller physical sample comparison chart
Fig.3 Different defects of roller side samples
Fig.4 Original image’s core detection area and image after Gaussian filtering
Fig.5 Binarized image and contour feature
Fig.6 Upper side external fillet profile and image with black border
Fig.7 Example of defective area data labeling
Fig.8 Inner critical state and outer critical state of pre-selection box
Fig.9 Defect collage comparison chart
Fig.10 Negative samples generated by defect transformation
Fig.11 Flow chart of overall inspection of bearing roller
Fig.12 Results of different data augmentation methods
$ \alpha $ ${P_{\rm{a}}}$ $ {P_{\text{c}}} $ $ {F_\delta } $
0.50 0.927 0.979 0.966
0.75 0.922 0.956 0.950
1.00 0.929 0.958 0.953
1.25 0.927 0.949 0.947
1.50 0.924 0.926 0.932
Tab.1 Influence of different alpha values of loss function on model indicators
组别 $ {P_a} $ $ {P_{\text{c}}} $
原图 0.762 0.926
J组 0.879 0.934
K组 0.905 0.945
Tab.2 Comparison of original image and results of two cropping methods on test set
色彩抖动实验 清晰度实验
u v 训练集 $ {P_a} $ 测试集 $ {P_a} $ u v 训练集 $ {P_a} $ 测试集 $ {P_a} $
0 0 0.931 0.905 0 0 0.931 0.905
0.5 0.1 0.942 0.919 0.5 0.1 0.935 0.903
0.5 0.2 0.942 0.920 0.5 0.2 0.937 0.908
0.5 0.3 0.941 0.921 0.5 0.3 0.938 0.914
0.5 0.4 0.936 0.917 0.5 0.4 0.938 0.914
0.5 0.5 0.931 0.914 0.5 0.5 0.938 0.914
0.5 0.6 0.931 0.913 0.5 0.6 0.938 0.916
0.5 0.7 0.931 0.911 0.5 0.7 0.937 0.919
0.5 0.8 0.926 0.910 0.5 0.8 0.937 0.920
0.5 0.9 0.921 0.910 0.5 0.9 0.937 0.921
0.5 1.0 0.916 0.909 0.5 1.0 0.936 0.905
0.1 0.3 0.937 0.919 0.1 0.9 0.929 0.905
0.2 0.3 0.936 0.911 0.2 0.9 0.938 0.914
0.3 0.3 0.935 0.903 0.3 0.9 0.938 0.919
0.4 0.3 0.938 0.912 0.4 0.9 0.939 0.924
0.6 0.3 0.936 0.915 0.6 0.9 0.943 0.922
0.7 0.3 0.930 0.908 0.7 0.9 0.943 0.929
0.8 0.3 0.931 0.912 0.8 0.9 0.943 0.936
0.9 0.3 0.932 0.916 0.9 0.9 0.941 0.930
Tab.3 Roller sample color jitter and clarity experiment
u 训练集 测试集
0.1 0.919 0.915
0.2 0.925 0.924
0.3 0.928 0.926
0.4 0.941 0.908
0.5 0.934 0.913
0.6 0.947 0.904
0.7 0.961 0.905
0.8 0.950 0.901
0.9 0.955 0.891
Tab.4 Roller sample defect transformation experiment
Fig.13 Accuracy and loss variation under different data augmentation methods
数据增强方案 ${P_{\rm{a}}}$ $ {P_{\text{c}}} $ R $ {F_\delta } $
原图 0.762 0.926 0.736 0.881
目标裁剪 0.871 0.937 0.886 0.926
目标裁剪+旋转+填充（基准） 0.896 0.940 0.891 0.930
基准+色彩抖动 0.919 0.934 0.935 0.934
基准+清晰度 0.931 0.974 0.924 0.964
基准+损伤变换 0.926 0.960 0.928 0.953
基准+色彩抖动+清晰度+损伤变换 0.952 0.977 0.930 0.967
Tab.5 Performance test results of different data augmentation methods
t $ {P_a} $ $ {P_{\text{c}}} $ t ${P_a} $ ${P_{\text{c}}} $
0.5 0.952 0.977 0.8 0.934 0.992
0.6 0.947 0.981 0.9 0.888 0.998
0.7 0.940 0.985 ? ? ?
Tab.6 Influence of different positive sample thresholds on accuracy and precision
[1]   何斌, 戚佳杰, 黎明和 小波分析在滚动轴承故障诊断中的应用研究[J]. 浙江大学学报:工学版, 2009, 43 (7): 1218- 1221
HE Bin, QI Jia-jie, LI Ming-he Application and research of wavelet analysis in fault diagnosis of roller bearings[J]. Journal of Zhejiang University: Engineering Science, 2009, 43 (7): 1218- 1221
[2]   梁子千, 玄文博, 王婷, 等 基于二维阻抗特征的管道环焊缝缺陷涡流检测[J]. 仪器仪表学报, 2017, 38 (9): 2138- 2145
LIANG Zi-qian, XUAN Wen-bo, WANG Ting, et al Eddy current NDt for the cracks of girth welds of pipes based on 2D impedance characteristics[J]. Chinese Journal of Scientific Instrument, 2017, 38 (9): 2138- 2145
doi: 10.3969/j.issn.0254-3087.2017.09.006
[3]   HANSEN J The eddy current inspection method[J]. Insight, 2004, 46 (5): 279- 281
doi: 10.1784/insi.46.5.279.55563
[4]   刘旭, 夏金东, 弓乐, 等 射频信号在超声检测缺陷识别中的应用研究[J]. 机械工程学报, 2002, 38 (4): 84- 87
LIU Xu, XIA Jin-dong, GONG Yue, et al Study on the application of radio-frequency signal in flaw classification of ultrasonic testing[J]. Chinese Journal of Mechanical Engineering, 2002, 38 (4): 84- 87
doi: 10.3321/j.issn:0577-6686.2002.04.018
[5]   杨辰龙, 周晓军 复杂曲面工件的超声无损检测系统研制[J]. 中国机械工程, 2005, 16 (18): 1622- 1625
YANG Chen-long, ZHOU Xiao-jun Development of ultrasonic none-destructive test systems for complex curved surface parts[J]. China Mechanical Engineering, 2005, 16 (18): 1622- 1625
doi: 10.3321/j.issn:1004-132X.2005.18.007
[6]   HAYNES C, TODD M, NADABE T, et al Monitoring of bearing failure in composite bolted connections using ultrasonic guided waves: a parametric study[J]. Structural Health Monitoring, 2014, 13 (1): 94- 105
doi: 10.1177/1475921713510756
[7]   王姮, 卜燕, 张华, 等 基于分形维数的磁痕图像缺陷检测[J]. 计算机应用研究, 2015, 32 (2): 603- 605
WANG Heng, BU Yan, ZHANG Hua, et al Defect detection of magnetic particle indication image based on fractal dimension[J]. Application Research of Computers, 2015, 32 (2): 603- 605
doi: 10.3969/j.issn.1001-3695.2015.02.063
[8]   陈昊, 张奔, 黎明, 等 基于图像光流的轴承滚子表面缺陷检测[J]. 仪器仪表学报, 2018, 39 (6): 198- 206
CHEN Hao, ZHANG Ben, LI Ming, et al Surface defect detection of bearing roller based on image optical flow[J]. Chinese Journal of Scientific Instrument, 2018, 39 (6): 198- 206
doi: 10.19650/j.cnki.cjsi.J1803226
[9]   CHAN F W Y Reflective fringe pattern technique for subsurface crack defection[J]. NDT and E International, 2008, 41 (8): 602- 610
doi: 10.1016/j.ndteint.2008.06.003
[10]   段志达. 基于机器视觉的轴承缺陷检测[D]. 安徽: 安徽工程大学, 2020: 23.
DUAN Zhi-da. Bearing defect detection based on machine vision [D]. Anhui: Anhui Polytechnic University, 2020: 23.
[11]   尚鲁强. 基于机器视觉的复杂曲面工件表面缺陷检测技术与系统[D]. 江西: 华东交通大学, 2020: 31.
SHAGN Lu-qiang. Technology and system for detecting surface defects of complex curved workpieces based on machine vision [D]. Jiangxi: East China Jiaotong University, 2020: 31.
[12]   苏俊宏, 刘胜利 圆柱型高精密零件表面缺陷检测及形貌分析[J]. 激光与光电子学进展, 2014, 51 (4): 150- 154
SU Jun-hong, LIU Sheng-li Measure system of surface flaw and morphology analysis of cylindrical high precision parts[J]. Laser and Optoelectronics Progress, 2014, 51 (4): 150- 154
[13]   文生平, 周正军, 张啸言, 等 基于计算机视觉的轴承滚子表面缺陷在线检测系统[J]. 华南理工大学学报:自然科学版, 2020, 48 (10): 76- 87
WEN Sheng-ping, ZHOU Zheng-jun, ZHANG Xiao-yan, et al On-line detection system for surface defects of bearing rollers based on computer vision[J]. Journal of South China University of Technology: Natural Science Edition, 2020, 48 (10): 76- 87
[14]   PRAPPACHER N, BULLMANN M, BOHN G, et al Defect detection on rolling element surface scans using neural image segmentation[J]. Applied Sciences, 2020, 10 (9): 3290
doi: 10.3390/app10093290
[15]   XU X , ZHENG H, GUO Z, et al. SDD-CNN: small data-driven convolution neural networks for subtle roller defect inspection[J]. Applied Sciences, 2019, 9 (7): 1364
doi: 10.3390/app9071364
[16]   LE X, MEI J, ZHANG H, et al A learning-based approach for surface defect detection using small image datasets[J]. Neurocomputing, 2020, (408): 112- 120
[17]   HU J, YAN P, SU Y, et al A method for classification of surface defect on metal workpieces based on twin attention mechanism generative adversarial network[J]. IEEE Sensors, 2021, 21 (12): 13430- 13441
doi: 10.1109/JSEN.2021.3066603
[18]   FANG H, SUN J, WANG R, et al. InstaBoost: boosting instance segmentation via probability map guided copy-pasting [C]// Proceedings of the IEEE/CVF International Conference on Computer Vision. Korea: Seoul, 2019: 682-691.
[19]   VINCENT O R, FOLORUNSO O A. Descriptive algorithm for sobel image edge detection [C]// Proceedings of Informing Science and IT Education Conference (InSITE). USA: Macon, 2009, 40: 97-107.
[20]   BASU M Gaussian-based edge-detection methods-a survey[J]. IEEE Transactions on Systems, Man, and Cybernetics, Part C, 2002, 32 (3): 252- 260
doi: 10.1109/TSMCC.2002.804448
[21]   HARALICK R M, STERNBERG S R, ZHUANG X Image analysis using mathematical morphology[J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1987, (4): 532- 550
[22]   SUZUKI S Topological structural analysis of digitized binary images by border following[J]. Computer Vision, Graphics and Image Processing, 1985, 30 (1): 32- 46
doi: 10.1016/0734-189X(85)90016-7
[23]   ZHENG Z , WANG P , LIU W , et al. Distance-IoU loss: faster and better learning for bounding box regression [C]// AAAI Conference on Artificial Intelligence. USA: New York, 2020, 34(7): 12993-13000.
[24]   XIE S, GIRSHICK R, DOLLAR P, et al. Aggregated residual transformations for deep neural networks [C]// Proceedings of the IEEE Conference on Computer Vision and Pattern Recognition. USA: Honolulu, 2017: 1492-1500.
[1] Jian-zhao ZHANG,Ji-chang GUO,Yu-dong WANG. Underwater image enhancement algorithm via fusing reverse medium transmission map[J]. Journal of ZheJiang University (Engineering Science), 2023, 57(5): 921-929.
[2] Huan LAN,Jian-bo YU. Steel surface defect detection based on deep learning 3D reconstruction[J]. Journal of ZheJiang University (Engineering Science), 2023, 57(3): 466-476.
[3] Fan YANG,Bo NING,Huai-qing LI,Xin ZHOU,Guan-yu LI. Multimodal image retrieval model based on semantic-enhanced feature fusion[J]. Journal of ZheJiang University (Engineering Science), 2023, 57(2): 252-258.
[4] Guo-hua ZHOU,Jian-wei LU,Tong-guang NI,Xue-long HU. Hierarchical nonlinear subspace dictionary learning[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(6): 1159-1167.
[5] Xue-qin ZHANG,Tian-ren LI. Breast cancer pathological image classification based on Cycle-GAN and improved DPN network[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(4): 727-735.
[6] Na ZHANG,Xu-lei QI,Xiao-an BAO,Biao WU,Xiao-mei TU,Yu-ting JIN. Single-stage object detection algorithm based on optimizing position prediction[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(4): 783-794.
[7] Xin-yu HUANG,Fan YOU,Pei ZHANG,Zhao ZHANG,Bai-li ZHANG,Jian-hua LV,Li-zhen XU. Silent liveness detection algorithm based on multi classification and feature fusion network[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(2): 263-270.
[8] Tian-le YUAN,Ju-long YUAN,Yong-jian ZHU,Han-chen ZHENG. Surface defect detection algorithm of thrust ball bearing based on improved YOLOv5[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(12): 2349-2357.
[9] Yun-zuo ZHANG,Wei GUO,Zhao-quan CAI,Wen-bo LI. Remote sensing image target detection combining multi-scale and attention mechanism[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(11): 2215-2223.
[10] Ming LI,Li-juan DUAN,Wen-jian WANG,Qing EN. Brain functional connections classification method based on significant sparse strong correlation[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(11): 2232-2240.
[11] Ze-xin XU,Li-juan DUAN,Wen-jian WANG,Qing EN. Code vulnerability detection method based on contextual feature fusion[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(11): 2260-2270.
[12] Dong-jie YANG,Xian-jun GAO,Shu-hao RAN,Guang-bin ZHANG,Ping WANG,Yuan-wei YANG. Building extraction based on multiple multiscale-feature fusion attention network[J]. Journal of ZheJiang University (Engineering Science), 2022, 56(10): 1924-1934.
[13] Zhi-chao CHEN,Hai-ning JIAO,Jie YANG,Hua-fu ZENG. Garbage image classification algorithm based on improved MobileNet v2[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(8): 1490-1499.
[14] Yue-lin CHEN,Wen-jing TIAN,Xiao-dong CAI,Shu-ting ZHENG. Text matching model based on dense connection networkand multi-dimensional feature fusion[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(12): 2352-2358.
[15] Xue-yun CHEN,Jin XIA,Ke DU. Overhead transmission line detection based on multiple linear-feature enhanced detector[J]. Journal of ZheJiang University (Engineering Science), 2021, 55(12): 2382-2389.