Please wait a minute...
浙江大学学报(工学版)
浙江大学学报(工学版)  2021, Vol. 55 Issue (2): 280-288    DOI: 10.3785/j.issn.1008-973X.2021.02.008
机械工程     
基于动态贝叶斯网络的变幅载荷下疲劳裂纹扩展预测方法
王泓晖1(),房鑫1,李德江2,刘贵杰1
1. 中国海洋大学 工程学院，山东 青岛 266100
2. 中集来福士海洋工程有限公司，山东 烟台 264000
Fatigue crack growth prediction method under variable amplitude load based on dynamic Bayesian network
Hong-hui WANG1(),Xin FANG1,De-jiang LI2,Gui-jie LIU1
1. College of Engineering, Ocean University of China, Qingdao 266100, China
2. Yantai CIMC-Raffles Offshore Co. Ltd, Yantai 264000, China
 全文: PDF(1436 KB)   HTML
摘要：

针对当前疲劳裂纹扩展预测研究中较少考虑不确定因素而导致预测结果偏差大的问题，提出基于动态贝叶斯网络（DBN）的疲劳裂纹扩展预测方法. 以变幅载荷作用下的疲劳裂纹扩展为具体对象，利用统一疲劳寿命预测（UFLP）模型构建疲劳裂纹扩展的物理状态方程；分析疲劳裂纹扩展过程典型不确定因素之间的联系，基于动态贝叶斯网络建立疲劳裂纹扩展动态性能退化模型；采用粒子滤波（PF）推断算法，向动态性能退化模型输入裂纹观测数据，修正预测结果，降低不确定因素的影响. 根据已有的裂纹扩展实验数据，给出具有不确定因素疲劳裂纹扩展预测的仿真算例，结果表明，所提出的基于动态贝叶斯网络的变幅载荷作下疲劳裂纹扩展预测方法较现有方法能够取得更好的预测精度.
关键词： 疲劳裂纹扩展;  不确定性因素;  动态贝叶斯网络;  变幅载荷;  粒子滤波    
Abstract:

A method of fatigue crack growth prediction based on dynamic Bayesian network (DBN) was proposed to address the problem of large deviation of prediction results due to less consideration of uncertainties in the current study of fatigue crack growth prediction. The physical state equation of fatigue crack expansion was constructed using the unified fatigue life prediction (UFLP) model using the fatigue crack expansion under variable amplitude load as the specific object. The link between typical uncertainties in the fatigue crack growth process was analyzed, and the degradation model of fatigue crack growth was built based on the DBN. Finally, the particle filter (PF) algorithm was used to input the crack observation data into the dynamic degradation model, and the prediction results were corrected to reduce the influence of uncertainties. A simulation example with uncertainty factor fatigue crack expansion prediction was given based on the existing experimental data on crack growth, and results show that the DBN-based method for fatigue crack growth prediction can achieve better prediction accuracy than existing methods.
Key words: fatigue crack growth    uncertainty factor    dynamic Bayesian network    variable amplitude load    particle filter
收稿日期: 2020-03-16 出版日期: 2021-03-09
CLC:  U 661  
基金资助: 国家重点研发计划资助项目（2019YFE0105100）；The National Key Research and Development Program of China (2019YFE0105100)
作者简介: 王泓晖（1989—），男，讲师，从事疲劳裂纹扩展研究. orcid.org/0000-0003-4336-7286. E-mail： honghui264@163.com
服务　　
把本文推荐给朋友
加入引用管理器
E-mail Alert
作者相关文章　　
王泓晖
房鑫
李德江
刘贵杰

引用本文:

王泓晖,房鑫,李德江,刘贵杰. 基于动态贝叶斯网络的变幅载荷下疲劳裂纹扩展预测方法[J]. 浙江大学学报(工学版), 2021, 55(2): 280-288.

Hong-hui WANG,Xin FANG,De-jiang LI,Gui-jie LIU. Fatigue crack growth prediction method under variable amplitude load based on dynamic Bayesian network. Journal of ZheJiang University (Engineering Science), 2021, 55(2): 280-288.

链接本文:

http://www.zjujournals.com/eng/CN/10.3785/j.issn.1008-973X.2021.02.008    或    http://www.zjujournals.com/eng/CN/Y2021/V55/I2/280

图 1  不确定性因素分类
参数 取值
$A$ 1.42×10?8
$m$ 1.4903
$n$ 6
${r_{\rm{e} } }/{\rm{m} }$ 10?6
${K_{ {\rm{IC} } } }/\left( { {\rm{MPa} } \cdot \sqrt {\rm{m} } } \right)$ 26.72
$\Delta {K_{ {\rm{eff} } } }/\left( { {\rm{MPa} } \cdot \sqrt {\rm{m} } } \right)$ 3.6
$\gamma $ 15.93
表 1  D16Cz合金板中心裂纹扩展模型参数值
图 2  参数 $m$对于裂纹扩展的影响
图 3  参数 $\gamma $对于裂纹扩展的影响
图 4  DBN疲劳裂纹扩展模型
图 5  PF流程图
%
w(Si) w(Fe) w(Cu) w(Mn) w(Mg) w(Cr) w(Zn) w(Ti) 其他
0.70~1.30 0.50 0.10 0.40~1.00 0.60~1.20 0.25 0.20 0.10 0.05
表 2  AlMgSi1-T6铝合金的化学组成成分
${\sigma _{\rm{u}}}$/MPa ${\sigma _{\rm{Y}}}$/MPa n′ ${K_{{\rm{IC}}}}$/MPa $\Delta {K_{{\rm{th}}}}$/ $({\rm{MPa} }\cdot\sqrt{\rm{ m} })$
$300.0 \pm 2.5$ $245.0 \pm 2.7$ 0.064 32 2.184?1.007R[33]
表 3  AlMgSi1-T6铝合金的物理性能
${S_{ {\rm{min} } } } /{\rm{MPa}}$ ${S_{ {\rm{max} } } } /{\rm{MPa}}$ $\Delta S /{\rm{MPa}}$ ${{R} }$ ${S_{ {\rm{OL} } } }/{\rm{MPa}}$ ${N_1}$ ${N_2}$
1.5 30.0 28.5 0.05 45.0 1000 500
表 4  周期性高载疲劳试验载荷大小
图 6  每个检查点后的 $A$的概率分布
图 7  每个检查点后的 $m$的概率分布
图 8  每个检查点后的 $\Delta {K_{{\rm{eff}}}}$的概率分布
图 9  每个检查点后的 $\gamma $的概率分布
图 10  每个检查点后的 $\xi $的概率分布
图 11  裂纹增长预测对比
1 FRANCE E J The Alexander L. Kielland disaster revisited: a review by an experienced welding engineer of the catastrophic North Sea platform collapse[J]. Practical Failure Analysis, 2019, 19 (4): 875- 881
doi: 10.1007/s11668-019-00680-4
2 张鼎, 黄小平 复杂载荷作用下潜艇结构疲劳裂纹扩展预报方法[J]. 舰船科学技术, 2012, 34 (2): 11- 16
ZHANG Ding, HUANG Xiao-ping Procedure to predict fatigue crack growth of submarine structures under complex loading conditions[J]. Ship Science and Technology, 2012, 34 (2): 11- 16
doi: 10.3404/j.issn.1672-7649.2012.02.002
3 何文涛, 刘敬喜, 解德 船体纵骨典型节点疲劳裂纹扩展寿命评估[J]. 船舶力学, 2016, 20 (11): 1475- 1484
HE Wen-tao, LIU Jing-xi, XIE De Life assessment of fatigue crack growth of typical details in hull longitudinals[J]. Journal of Ship Mechanics, 2016, 20 (11): 1475- 1484
4 陈飞宇, 卢丙举, 赵世平, 等 基于裂纹扩展的海洋平台疲劳可靠性分析[J]. 船舶工程, 2019, 41 (Suppl.2): 53- 57
CHEN Fei-yu, LU Bing-ju, ZHAO Shi-ping, et al Reliability assessment and fatigue life of jacket platform based on the theory of crack propagation[J]. Ship Engineering, 2019, 41 (Suppl.2): 53- 57
5 钱怡, 崔维成 变幅载荷下疲劳裂纹扩展规律试验研究综述[J]. 船舶力学, 2010, 14 (5): 556- 565
QIAN Yi, CUI Wei-cheng An overview on experimental investigation on variable amplitude fatigue crack growth rule[J]. Joumal of Ship Mechanics, 2010, 14 (5): 556- 565
doi: 10.3969/j.issn.1007-7294.2010.05.015
6 WHEELER O E Spectrum loading and crack growh[J]. Journal of Fluids Engineering, 1972, 94 (1): 181- 186
7 CUI W, WANG F, HUANG X A unified fatigue life prediction method for marine structures[J]. Marine Structures, 2011, 24 (2): 153- 181
doi: 10.1016/j.marstruc.2011.02.007
8 DIRIK H, YALINKAYA T Crack path and life prediction under mixed mode cyclic variable amplitude loading through XFEM[J]. International Journal of Fatigue, 2018, 114: 34- 50
doi: 10.1016/j.ijfatigue.2018.04.026
9 LESIUK G, SMOLNICKI M, MECH R, et al Analysis of fatigue crack growth under mixed mode (I+II) loading conditions in rail steel using CTS specimen[J]. Engineering Failure Analysis, 2020, 109: 104354
doi: 10.1016/j.engfailanal.2019.104354
10 张宝峰, 曲淑英, 邵永波, 等 海洋平台K型管节点的疲劳裂纹扩展分析I: 试验测试[J]. 计算力学学报, 2007, (5): 643- 647
ZHANG Bao-feng, QU Shu-ying, SHAO Yong-bo, et al Fatigue crack propagation analysis of tubular K joints1: experimental test[J]. Chinese Journal of Computational Mechanics, 2007, (5): 643- 647
doi: 10.3969/j.issn.1007-4708.2007.05.018
11 曲淑英, 邵永波, 张宝峰, 等 海洋平台K型管节点的疲劳裂纹扩展分析II: 数值分析[J]. 计算力学学报, 2007, (6): 800- 805
QU Shu-ying, SHAO Yong-bo, ZHANG Bao-feng, et al Fatigue crack propagation analysis of tubular K-joints1: numerical[J]. Chinese Journal of Computational Mechanics, 2007, (6): 800- 805
doi: 10.3969/j.issn.1007-4708.2007.06.016
12 林琳, 罗斌, 钟诗胜 基于视情维修的机队维修决策方法[J]. 计算机集成制造系统, 2019, 25 (3): 661- 672
LIN Lin, LUO Bin, ZHONG Shi-sheng Research on maintenance decision-making based on CBM for a fleet[J]. Computer Integrated Manufacturing Systems, 2019, 25 (3): 661- 672
13 马涛, 孙振国, 陈强 基于几何与纹理特征相融合的磁粉探伤裂纹提取算法[J]. 清华大学学报: 自然科学版, 2018, 58 (1): 50- 54
MA Tao, SUN Zhen-guo, CHEN Qiang Crack detection algorithm for fluorescent magnetic particle inspection based on shape and texture features[J]. Journal of Tsinghua University: Science and Technology, 2018, 58 (1): 50- 54
14 SU Z, ZHOU C, HONG M, et al Acousto-ultrasonic based damage characterization: linear versus nonlinear signal features[J]. Mechanical Systems and Signal Processing, 2014, 45 (1): 225- 239
doi: 10.1016/j.ymssp.2013.10.017
15 HANSEN K, GUDMESTAD O T. Reassessment of jacket type of platforms subject to wave-in-deck forces: current practice and future development [C]// The 11th International Offshore and Polar Engineering Conference. Norway: International Society of Offshore and Polar Engineers, 2001: 482-489.
16 王晓煜, 高斯佳, 詹益, 等 铝板损伤的超声Lamb波与机磁阻抗集成检测[J]. 无损检测, 2019, 41 (7): 23- 29
WANG Xiao-yu, GAO Si-jia, ZHAN Yi, et al Damage detection of aluminum plate based on integrated ultrasonic lamb wave and magneto-mechanical impedance[J]. Nondestructive Testing, 2019, 41 (7): 23- 29
doi: 10.11973/wsjc201907006
17 曲文声, 王寿军, 穆为磊, 等 基于声发射技术的海洋平台材料疲劳损伤检测[J]. 无损检测, 2016, 38 (10): 10- 13
QU Wen-sheng, WANG Shou-jun, MU Wei-lei, et al Fatigue failure detection of the offshore platform materal based on acoustic emission[J]. Nondestructive Testing, 2016, 38 (10): 10- 13
doi: 10.11973/wsjc201610003
18 LI C, MAHADEVAN S, LING Y, et al Dynamic Bayesian network for aircraft wing health monitoring digital twin[J]. AIAA Journal, 2017, 55 (3): 930- 941
doi: 10.2514/1.J055201
19 BARTRAM G, MAHADEVAN S Integration of heterogeneous information in SHM models[J]. Structural Control and Health Monitoring, 2014, 21 (3): 403- 422
doi: 10.1002/stc.1572
20 宋悦, 时祎瑜, 于劲松, 等 基于数字孪生的光电探测系统性能预测[J]. 计算机集成制造系统, 2019, 25 (6): 1559- 1567
SONG Yue, SHI Yi-yu, YU Jin-song, et al Application of digital twin model in performance prediction of electro-optical detection system[J]. Computer Integrated Manufact uring Systems, 2019, 25 (6): 1559- 1567
21 杨旗, 薛定宇 基于双尺度动态贝叶斯网络及多信息融合的步态识别[J]. 电子与信息学报, 2012, 34 (5): 1148- 1153
YANG Qi, XUE Ding-yu Gait recognition based on two-scale dynamic bayesian network and more information fusion[J]. Journal of Electronics and Information Technology, 2012, 34 (5): 1148- 1153
22 CUI W C, HUANG X P A general constitutive relation for fatigue crack growth analysis of metal structures[J]. Acta Metallurgica Sinica, 2003, 16 (5): 342- 54
23 CHEN F, WANG F, CUI W C Fatigue life prediction of engineering structures subjected to variable amplitude loading using the improved crack growth rate model[J]. Fatigue & Fracture of Engineering Materials & Structures, 2012, 35 (3): 278- 90
24 SANKARARAMAN S, LING Y, MAHADEVAN S Uncertainty quantification and model validation of fatigue crack growth prediction[J]. Engineering Fracture Mechanics, 2011, 78 (7): 1487- 1504
doi: 10.1016/j.engfracmech.2011.02.017
25 WANG Y F, WANG Y W, WU X Y, et al A study on the relation between threshold effective stress intensity factor range and load ratio[J]. Journal of Ship Mechanics, 2008, (3): 440- 453
26 WANG F, CHENG F L, CUI W C Applicability of the improved crack growth rate model and its parameters estimation method[J]. Journal of Ship Mechanics, 2010, 14 (3): 252- 262
27 WANG Y Y, WANG F, CUI W C Fatigue reliability analysis for the manned cabin of deep manned submersibles based on the unified fatigue life prediction method[J]. Journal of Ship Mechanics, 2016, 20 (3): 335- 347
28 SCHIJVE J, SKORUPA M, SKORUPA A, et al Fatigue crack growth in the aluminium alloy D16 under constant and variable amplitude loading[J]. International Journal of Fatigue, 2004, 26 (1): 1- 15
doi: 10.1016/S0142-1123(03)00067-7
29 WANG F, CUI W On the engineering approach to estimating the parameters in an improved crack growth rate model for fatigue life prediction[J]. Ships and Offshore Structures, 2010, 5 (3): 227- 241
doi: 10.1080/17445300903439207
30 厉海涛, 金光, 周经伦, 等 贝叶斯网络推理算法综述[J]. 系统工程与电子技术, 2008, (5): 935- 939
LI Hai-tao, JIN Guang, ZHOU Jing-lun, et al Survey of Bayesian network inference algorithms[J]. Systems Engineering and Electronics, 2008, (5): 935- 939
doi: 10.3321/j.issn:1001-506X.2008.05.039
31 关雪雪, 陈建桥, 郑瑶辰, 等 预测结构性能退化的混合粒子滤波方法[J]. 力学学报, 2018, 50 (3): 677- 687
GUAN Xue-xue, CHEN Jian-qiao, ZHENG Yao-chen, et al A combined Particle Filter method for predicting structural performance degradation[J]. Chinese Journal of Theoretical and Applied Mechanics, 2018, 50 (3): 677- 687
doi: 10.6052/0459-1879-18-014
32 BORREGO L P, COSTA J M, FERREIRA J M Fatigue crack growth in thin aluminium alloy sheets under loading sequences with periodic overloads[J]. Thin-Walled Structures, 2004, 43 (5): 772- 788
[1] 蒋鑫龙, 陈益强, 刘军发, 忽丽莎, 沈建飞. 面向自闭症患者社交距离认知的可穿戴系统[J]. 浙江大学学报(工学版), 2017, 51(4): 637-647.
[2] 李明, 刘扬, 唐雪松. 疲劳裂纹的跨尺度分析[J]. 浙江大学学报(工学版), 2017, 51(3): 524-531.
[3] 康轶非,宋永端,宋宇,闫德立. 不依赖里程计的机器人定位与地图构建[J]. J4, 2014, 48(3): 414-422.
[4] 陈家乾, 何衍, 蒋静坪. 基于权值平滑的改良FastSLAM算法[J]. J4, 2010, 44(8): 1454-1459.
[5] 谷雨, 李平, 韩波. 基于分层粒子滤波的地标检测与跟踪[J]. J4, 2010, 44(4): 687-691.
[6] 许士芳 谢立 刘济林. 基于MCMC粒子滤波的机器人定位[J]. J4, 2007, 41(7): 1083-1087.
[7] 武二永 项志宇 沈敏一 刘济林. 大规模环境下基于激光雷达的机器人SLAM算法[J]. J4, 2007, 41(12): 1982-1986.
[8] 周迅 俞小莉. 曲轴疲劳裂纹扩展速率测量的扫频法[J]. J4, 2007, 41(11): 1886-1892.
[9] 洪涛 王申康. 基于图像特征分析的人体正面运动跟踪研究[J]. J4, 2005, 39(6): 845-848.