Fatigue crack growth prediction method under variable amplitude load based on dynamic Bayesian network

doi:10.3785/j.issn.1008-973X.2021.02.008

Journal of ZheJiang University (Engineering Science)

2021, Vol. 55

Issue (2): 280-288 DOI: 10.3785/j.issn.1008-973X.2021.02.008

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

Download:

HTML

PDF(1436KB) HTML
Export: BibTeX | EndNote (RIS)

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

Received: 16 March 2020 Published: 09 March 2021

CLC:

U 661

Fund: 国家重点研发计划资助项目（2019YFE0105100）；The National Key Research and Development Program of China (2019YFE0105100)

	Service
	E-mail this article
	Add to my bookshelf
	Add to citation manager
	E-mail Alert
	RSS
	Articles by authors
	Hong-hui WANG
	Xin FANG
	De-jiang LI
	Gui-jie LIU

Cite this article:

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.

URL:

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

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

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

Fig.1 Classification of uncertainty factors

Tab.1 Parameter values of central crack growth model for D16Cz alloy

Fig.2 Effect of parameters m on crack growth

Fig.3 Effect of parameters

$\gamma $

on crack growth

Fig.4 DBN fatigue crack growth model

Fig.5 Flow chart of particle filtering

Tab.2 Chemical composition of AlMgSi1-T6 aluminum alloy

Tab.3 Physical properties of AlMgSi1-T6 aluminum alloy

Tab.4 Load size of cyclic high-load fatigue test

Fig.6 PDF of parameters

$A$

after each checkpoint

Fig.7 PDF of parameters

$m$

after each checkpoint

Fig.8 PDF of parameters

$\Delta {K_{{\rm{eff}}}}$

after each checkpoint

Fig.9 PDF of parameters

$\gamma $

after each checkpoint

Fig.10 PDF of parameters

$\xi $

after each checkpoint

Fig.11 Comparison of crack growth prediction


[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]	JIANG Xin-long, CHEN Yi-qiang, LIU Jun-fa, HU Li-sha, SHEN Jian-fei. Wearable system to support proximity awareness for people with autism[J]. Journal of ZheJiang University (Engineering Science), 2017, 51(4): 637-647.

[2]	KANG Yi-fei, SONG Yong-duan, SONG Yu, YAN De-li. Simultaneous localization and mapping without relying on odometer[J]. Journal of ZheJiang University (Engineering Science), 2014, 48(3): 414-422.

[3]	CHEN Jia-Gan, HE Yan, JIANG Jing-Ping. Improved FastSLAM algorithm based on importance weight smoothing[J]. Journal of ZheJiang University (Engineering Science), 2010, 44(8): 1454-1459.

[4]	GU Yu, LI Beng, HAN Bei. Landmark detection and tracking based on layered particle filter[J]. Journal of ZheJiang University (Engineering Science), 2010, 44(4): 687-691.

Viewed

Full text

Abstract

Cited

Shared

Discussed