基于卷积神经网络的白蚀缺陷超声探测

朱琦; 许多; 张元军; 李玉娟; 王文; 张海燕

doi:10.7498/aps.71.20221504

摘要

不同于经典滚动接触疲劳形成的缺陷, 亚表面白蚀缺陷会引起轴承零件的早期失效, 严重缩短零件的寿命. 它位于金属亚表面且尺寸微小, 难以使用常规手段实现检测. 白蚀缺陷成因尚不明确, 不同演化阶段的缺陷样品制备耗时费力. 本文建立了白蚀缺陷演化模型, 基于k空间伪谱法开展了水浸超声检测过程数值实验. 对于含裂纹的白蚀缺陷演化后期, 可以忽略内部晶粒结构建立均匀层状模型, 使用经典声压反射系数幅度谱获取裂纹深度, 误差为1.5%. 对于不含裂纹的其他白蚀缺陷状态, 则存在内部声阻抗差异较小, 频谱特征不再明显等问题. 基于维诺图(Voronoi)建立轴承晶粒模型, 利用晶粒对超声的背散射效应来放大微观结构信号. 高频情况下, 基于深度卷积神经网络的训练准确率达92%, 验证准确率为97%. 即使在较低检测频率下, 背散射信号较弱, 仍能获得81%的准确率. 为白蚀缺陷的早期检测提供了有效方案.

关键词:

Abstract

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

作者及机构信息

1.
上海大学机电工程与自动化学院, 上海　200444

2.
上海大学通信与信息工程学院, 上海　200444

通信作者: 朱琦, Q_ZHU@shu.edu.cn ; 张海燕, hyzh@shu.edu.cn

基金项目: 国家自然科学基金(批准号: 11904223, 12174245, 11874255)、国家重点研发计划(批准号: 2018YFB2000300)和精密测试技术及仪器国家重点实验室开放基金(批准号: pilab2209)资助的课题.

Authors and contacts

1.
School of Mechatronic and Automation Engineering, Shanghai University, Shanghai 200444, China

2.
School of Communication & Information Engineer, Shanghai University, Shanghai 200444, China

Corresponding author: Zhu Qi, Q_ZHU@shu.edu.cn ; Zhang Hai-Yan, hyzh@shu.edu.cn

Funds: Project supported by the National Natural Science Foundation of China (Grant Nos. 11904223, 12174245, 11874255), the National Key R&D Program of China (Grant No. 2018YFB2000300), and the Open Fund of State Key Laboratory of Precision Measuring Technology and Instruments, China (Grant No. pilab2209).

文章全文 : translate this paragraph

参考文献

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[2]	Linzmayer M, Sous C, Gutiérrez Guzmán F, Jacobs G 2021 Wear 480–481 203925 Google Scholar
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[19]	Cai Y, Song Y, Ni P, Liu X, Li X 2021 Ultrasonics 117 106552 Google Scholar
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施引文献

图 1 (a)多层结构的轴承截面; (b)三层介质简化模型

Fig. 1. (a) Multilayer cross-section of bearing; (b) simplified three-layer model.

下载: 全尺寸图片幻灯片

图 2 残差网络

Fig. 2. Residual network.

下载: 全尺寸图片幻灯片

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

Fig. 3. (a) Microscopic imaging of white etching defect; (b) ultrasonic simulation model of layered medium; (c) ultrasonic simulation model including grains.

下载: 全尺寸图片幻灯片

图 4 白蚀缺陷演化过程的5种不同状态

Fig. 4. Five different states of white etching defect during evolution.

下载: 全尺寸图片幻灯片

图 5 裂纹对声压反射系数幅度谱的影响

Fig. 5. Effect of crack on ultrasonic reflection coefficient amplitude spectrum.

下载: 全尺寸图片幻灯片

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

Fig. 6. (a) URCAS and (b) thickness calculation errors under different acoustic impedance ratios.

下载: 全尺寸图片幻灯片

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)

Fig. 7. Two-dimensional simulation of ultrasonic immersion testing of polycrystalline model (f = 100 MHz).

下载: 全尺寸图片幻灯片

图 8 不同频率下的背散射信号　(a)时域; (b)频域

Fig. 8. Backscattering signals under different frequencies: (a) Time domain; (b) frequency domain.

下载: 全尺寸图片幻灯片

图 9 (a)典型超声波形图 (f = 100 MHz); (b) STFT转换的时频图

Fig. 9. (a) Typical ultrasonic waveform (f = 100 MHz); (b) time-frequency diagram of STFT conversion.

下载: 全尺寸图片幻灯片

图 10 卷积神经网络结构图

Fig. 10. Convolutional neural network structure.

下载: 全尺寸图片幻灯片

图 11 100 MHz仿真模型的训练结果

Fig. 11. Training results of 100 MHz simulation model.

下载: 全尺寸图片幻灯片

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.

	声速 V/(m·s^–1)	密度 ρ/(kg·m^–3)	声阻抗 Z (MRayl)	晶粒尺寸
空气	340	1.29	$ 4.39\times {10}^{-4} $	—
水	1500	1000	1.5	—
白蚀组织	4350^[28]	7850^[9]	34.15	5—300 nm^[3]
轴承钢	5620	7840	44.06	正常15—25 μm, 细化后2—10 μm
不同晶粒取向的轴承钢	5510—6090	7410—8190	40.82—49.87	—

下载: 导出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

下载: 导出CSV

[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 物理学报 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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