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Unlike classical defects formed by rolling contact fatigue, white etching defect (WED) including white etching area and white etching crack will cause surface to spall in the early stage and the service life to shorten seriously. Located in the subsurface of bearings, the tiny size WED is difficult to detect by conventional ultrasonic methods. The root cause of WED generation remains unclear. It is time consuming and expensive to prepare samples during the evolution of such defects. For characterizing the WED at early stage, five evolving states concerning the existing microscopic information are established in this paper. The immersion ultrasonic inspection process is simulated based on k-space pseudo spectrum method. For the later evolutionary stage with crack, the bearing can be simplified into a homogeneous three-layer model by ignoring the internal grain structure. The crack depth is obtained by using the ultrasonic reflection coefficient amplitude spectrum (URCAS), with an error of 1.5%. For other states without crack, the spectrum characteristic is no longer evident with slight acoustic impedance difference between layers. The polycrystalline structure on a microscale is thus realized based on Voronoi diagram, from which the grain induced backscattering can be used to amplify the microstructure variations at different stages. The backscattering signal is influenced by the grain size and detection frequency from the simulation. Since a direct comparison of backscattering information among evolutionary stages is difficult, the five different evolutionary stages of WED are recognized with the help of deep learning. The received waveform is transformed into a time-frequency map by short-time Fourier transform. Based on RESNET network structure, the results show that the train accuracy and validation accuracy reach 92% and 97% respectively. This study provides a sound way to characterize WED, which is conducive to early failure prediction and residual life evaluation. -
Keywords:
- white etching defect /
- ultrasonic backscattering /
- deep learning /
- ultrasonic reflection coefficient amplitude spectrum
[1] Manieri F, Stadler K, Morales-Espejel G E, Kadiric A 2019 Int. J. Fatigue 120 107
Google Scholar
[2] Linzmayer M, Sous C, Gutiérrez Guzmán F, Jacobs G 2021 Wear 480–481 203925
Google Scholar
[3] Leung J F W, Bedekar V, Voothaluru R, Neu R W 2019 Metall. Mater. Trans. A 50 4949
Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
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Google Scholar
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图 1 (a)多层结构的轴承截面; (b)三层介质简化模型
Figure 1. (a) Multilayer cross-section of bearing; (b) simplified three-layer model.
图 2 残差网络
Figure 2. Residual network.
图 3 (a)白蚀缺陷显微成像; (b)层状介质的超声仿真模型; (c)包含晶粒特征的超声仿真模型
Figure 3. (a) Microscopic imaging of white etching defect; (b) ultrasonic simulation model of layered medium; (c) ultrasonic simulation model including grains.
图 4 白蚀缺陷演化过程的5种不同状态
Figure 4. Five different states of white etching defect during evolution.
图 5 裂纹对声压反射系数幅度谱的影响
Figure 5. Effect of crack on ultrasonic reflection coefficient amplitude spectrum.
图 6 不同声阻抗比下的(a)声压反射系数幅度谱和(b)厚度计算误差
Figure 6. (a) URCAS and (b) thickness calculation errors under different acoustic impedance ratios.
图 7 不同时刻下二维水浸超声检测在多晶模型的传播过程(f = 100 MHz)
Figure 7. Two-dimensional simulation of ultrasonic immersion testing of polycrystalline model (f = 100 MHz).
图 8 不同频率下的背散射信号 (a)时域; (b)频域
Figure 8. Backscattering signals under different frequencies: (a) Time domain; (b) frequency domain.
图 9 (a)典型超声波形图 (f = 100 MHz); (b) STFT转换的时频图
Figure 9. (a) Typical ultrasonic waveform (f = 100 MHz); (b) time-frequency diagram of STFT conversion.
图 10 卷积神经网络结构图
Figure 10. Convolutional neural network structure.
图 11 100 MHz仿真模型的训练结果
Figure 11. Training results of 100 MHz simulation model.
表 1 模型使用的声学参数及晶粒尺寸
Table 1. Acoustic characteristics and crystalline grain size of different materials.
DownLoad: CSV
表 2 不同频率超声换能器的卷积神经网络计算结果
Table 2. Convolution neural network calculation results of different frequency ultrasonic transducer.
检测
频率/MHz回合 训练集
准确率/%验证集
准确率/%损失 检测
频率/MHz回合 训练集
准确率/%验证集
准确率/%损失 100 1 35.24 58.70 2.05 50 1 34.21 56.48 2.20 10 83.19 91.28 0.46 10 81.68 88.70 0.48 20 88.20 95.66 0.31 20 86.40 93.77 0.33 30 92.42 97.31 0.21 30 90.01 95.16 0.22 25 1 32.14 50.02 2.32 15 1 26.80 45.96 2.35 10 74.65 87.95 0.63 10 70.52 83.66 0.68 20 81.43 90.71 0.43 20 79.92 88.50 0.45 30 85.99 93.40 0.22 30 81.03 91.02 0.22
DownLoad: CSV
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[1] Manieri F, Stadler K, Morales-Espejel G E, Kadiric A 2019 Int. J. Fatigue 120 107
Google Scholar
[2] Linzmayer M, Sous C, Gutiérrez Guzmán F, Jacobs G 2021 Wear 480–481 203925
Google Scholar
[3] Leung J F W, Bedekar V, Voothaluru R, Neu R W 2019 Metall. Mater. Trans. A 50 4949
Google Scholar
[4] Curd M E, Burnett T L, Fellowes J, Donoghue J, Yan P, Withers P J 2019 Acta Mater. 174 300
Google Scholar
[5] Evans M H, Walker J C, Ma C, Wang L, Wood R J K 2013 Mater. Sci. Eng. A 570 127
Google Scholar
[6] Lai J, Stadler K 2016 Wear 364–365 244
Google Scholar
[7] López-Uruñuela F J, Fernández-Díaz B, Pagano F, López-Ortega A, Pinedo B, Bayón R, Aguirrebeitia J 2021 Int. J. Fatigue 145 106091
Google Scholar
[8] Hu P, Turner J A, Tarawneh C, Wilson B, Fuller A J 2015 Proceeding of the Joint Rail Conference, San Jose, March 23–26, 2015 p5785
[9] Sreeraj K, Maheshwari H K, Rajagopal P, Ramkumar P 2021 Tribol. Int. 162 107134
Google Scholar
[10] Ma Z, Zhang W, Gao J, Lin L, Krishnaswamy S 2016 43rd Annual Review of Progress in Quantitative Nondestructive Evaluation, Atlanta, July 17–22, 2016 p1016
[11] Ma Z, Zhao Y, Luo Z, Lin L 2014 Ultrasonics 54 1005
Google Scholar
[12] Ma Z, Qi T, Lin L, Lei M 2022 Ultrasonics 119 106626
Google Scholar
[13] 李珊, 李雄兵, 宋永锋, 陈超 2018 67 107
Google Scholar
Li S, Li X B, Song Y F, Chen C 2018 Acta Phys. Sin. 67 107
Google Scholar
[14] Chen Y, Luo Z, Zhou Q, Zou L, Lin L 2015 Ultrasonics 59 31
Google Scholar
[15] Norouzian M, Islam S, Turner J A 2020 Ultrasonics 102 106032
Google Scholar
[16] 张永志, 辛全忠, 王永亮, 孔祥明, 刘昉, 杨再胜 2021 材料导报 35 24152
Google Scholar
Zhang Y Z, Xin Q Z, Wang Y L, Kong X M, Liu F, Yang Z S 2021 Mater. Rep. 35 24152
Google Scholar
[17] Liu H, Zhang Y 2019 Smart Mater. Struct. 29 015032
Google Scholar
[18] Pyle R J, Bevan R L T, Hughes R R, Rachev R K, Ali A A S, Wilcox P D 2021 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 68 1854
Google Scholar
[19] Cai Y, Song Y, Ni P, Liu X, Li X 2021 Ultrasonics 117 106552
Google Scholar
[20] Zhao Y, Lin L, Li X M, Lei M K 2010 NDT & E Int. 43 579
Google Scholar
[21] He K, Zhang X, Ren S, Sun J 2016 IEEE Conference on Computer Vision and Pattern Recognition (CVPR), Las Vegas, June 27–30, 2016 p770
[22] 郑战光, 汪兆亮, 冯强, 袁帅, 王佳祥 2016 广西大学学报(自然科学版) 41 460
Google Scholar
Zhen Z G, Wang Z L, Feng Q, Yuan S, Wang J X 2016 J. Guangxi Uni. ( Nat Sci Ed) 41 460
Google Scholar
[23] Weinzapfel N, Sadeghi F 2013 Tribol. Int. 59 210
Google Scholar
[24] Bai X, Tie B, Schmitt J H, Aubry D 2018 Ultrasonics 87 182
Google Scholar
[25] Yin A, Wang X, Glorieux C, Yang Q, Dong F, He F, Wang Y, Sermeus J, Van der Donck T, Shu X 2017 Ultrasonics 78 30
Google Scholar
[26] Dryburgh P, Smith R J, Marrow P, Lainé S J, Sharples S D, Clark M, Li W 2020 Ultrasonics 108 106171
Google Scholar
[27] Smith R L 1982 Ultrasonics 20 211
Google Scholar
[28] 魏勤, 卫婷, 董师润, 张海林 2012 江苏科技大学学报(自然科学版) 26 27
Google Scholar
Wei Q, Wei T, Dong S R, Zhang H L 2012 J. Jiangsu Univ. Sci. Technol. (Nat. Sci. Ed) 26 27
Google Scholar
[29] Martin E, Jaros J, Treeby B E 2020 IEEE Trans. Ultrason. Ferroelectr. Freq. Control 67 81
Google Scholar
[30] Gottlieb D, Tadmor E 1991 Math. Comput. 56 565
Google Scholar
[31] Tillett J C, Daoud M I, Lacefield J C, Waag R C 2009 J. Acous. Soc. Am. 126 1231
Google Scholar
[32] Loos J, Blass T, Franke J, Kruhoeffer W, Bergmann I 2016 J. Mech. Eng. Autom. 6 85
Google Scholar
[33] 章桢彦, Toni B, 唐瑜, 韦剑飞 2020 中国冶金 30 2
Google Scholar
Zhang Z Y, Toni B, Tang Y, Wei J F 2020 China Metallurgy 30 2
Google Scholar
[34] 宋永锋, 李雄兵, 吴海平, 司家勇, 韩晓芹 2016 金属学报 52 378
Google Scholar
Song Y F, Li X B, Wu H P, Si J Y, Han X Q 2016 Acta Metall. Sin. 52 378
Google Scholar
[35] Bai X T 2021 M. S. Thesis (Xi’an: Xi’an University of Technology) (in Chinese) [白旭天 2021 硕士学位论文 (西安: 西安理工大学)]
[36] Behnke M, Guo S, Guo W G 2021 Proc. Manuf. 53 656
Google Scholar
[37] Ha C, Tran V D, Ngo Van L, Than K 2019 Int. J. Approx. Reason. 112 85
Google Scholar
[38] Mutasa S, Sun S, Ha R 2020 Clin. Imaging 65 96
Google Scholar
[39] Song G, Qin D, Lyu Y, Hong G, Xu Y, Wu B, He C 2017 Int. J. Acoust. Vib. 22 511
Google Scholar
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