Prediction of fatigue crack propagation based on auxiliary particle filtering

YANG Weibo, YUAN Shenfang, QIU Lei, CHEN Jian

Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (5) : 114-119.

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PDF(967 KB)
Journal of Vibration and Shock ›› 2018, Vol. 37 ›› Issue (5) : 114-119.

 Prediction of fatigue crack propagation based on auxiliary particle filtering

  •   YANG Weibo, YUAN Shenfang, QIU Lei, CHEN Jian
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Abstract

Aiming at the lack of diversity in the particle filtering algorithm for fatigue crack life prediction, a method for prediction of fatigue crack propagation based on auxiliary particle filtering and structural health monitoring was proposed. Firstly, Paris rule of crack propagation was combined with the finite element method to build the state equation of crack propagation. Secondly, the active Lamb wave health monitoring technique was used to monitor the process of fatigue crack propagation. The time delay damage index was used to process Lamb wave signals, and then fit the function relation between crack length and damage index, the observation equation of crack propagation was established. The state space model for crack propagation was built through combining state equations and observation equations. Finally, the life prediction of hole-edge crack propagation was realized using the auxiliary particle filtering and the standard particle filtering, respectively. The comparison of the prediction results showed that the auxiliary particle filtering in fatigue crack propagation prediction of complex structures can effectively mitigate the lack of particle diversity combining the latest observation; its prediction results are more accurate; it is more applicable for realizing complex structures’ online fatigue life prediction.

 

Key words

 fatigue crack propagation / particle filtering / auxiliary particle filtering / structural health monitoring / Lamb wave

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YANG Weibo, YUAN Shenfang, QIU Lei, CHEN Jian.  Prediction of fatigue crack propagation based on auxiliary particle filtering[J]. Journal of Vibration and Shock, 2018, 37(5): 114-119

References

[1] Zhang H, Kang R, Pecht M. A hybrid prognostics and health management approach for condition-based maintenance[C]. 2009 IEEE International Conference on Industrial Engineering and Engineering Management. IEEE, 2009: 1165-1169.
[2] Pecht M, Gu J. Physics-of-failure-based prognostics for electronic products[J]. Transactions of the Institute of Measurement and Control, 2009, 31(3-4): 309-322.
[3] Sheppard J W, Kaufman M A, Wilmer T J. IEEE standards for prognostics and health management [J]. Aerospace and Electronic Systems Magazine, IEEE, 2009, 24(9): 34-41.
[4] Gordon N J, Salmond D J, Smith A F M. Novel approach to nonlinear/non-Gaussian Bayesian state estimation[C]. IEE Proceedings F (Radar and Signal Processing). IET Digital Library, 1993, 140(2): 107-113.
[5] Orchard M, Vachtsevanos G. A particle filtering approach for on-line failure prognosis in a planetary carrier plate [J]. International Journal of Fuzzy Logic and Intelligent Systems, 2007, 7(4): 221-7.
[6] Orchard M E, Vachtsevanos G J. A particle-filtering approach for on-line fault diagnosis and failure prognosis[J]. Transactions of the Institute of Measurement and Control, 2009, 31(3-4): 221-46.
[7] Cadini F, Zio E, Avram D. Monte Carlo-based filtering for fatigue crack growth estimation[J]. Probabilistic Engineering Mechanics, 2009, 24(3): 367-373.
[8] Zio E, Peloni G. Particle filtering prognostic estimation of the remaining useful life of nonlinear components[J]. Reliability Engineering & System Safety, 2011, 96(3): 403-409.
[9] 袁慎芳, 张华, 邱雷, 等.基于粒子滤波算法的疲劳裂纹扩展预测方法[J].航空学报, 2013, 34(12): 2740-2747.
Yuan S F, Zhang H, Qiu L, el at. A fatigue crack growth prediction method based on particle filter [J]. Acta Aeronautica et Astronautic Sinica, 2013, 34(12): 2740-2747
[10] Doucet A, Godsill S, Andrieu C. On sequential Monte Carlo sampling methods for Bayesian filtering[J]. Statistics and computing, 2000, 10(3): 197-208.
[11] Pitt M K, Shephard N. Filtering via simulation: Auxiliary particle filters[J]. Journal of the American statistical association, 1999, 94(446): 590-599.
[12] Pitt M K, Shephard N. Auxiliary variable based particle filters[M]. Sequential Monte Carlo methods in practice. Springer New York, 2001: 273-293.
[13] Sun J, Zuo H, Wang W, et al. Prognostics uncertainty reduction by fusing on-line monitoring data based on a state-space-based degradation model[J]. Mechanical Systems and Signal Processing, 2014, 45(2): 396-407.
[14] 曲先强, 马永亮, 崔洪斌, 等.Paris 公式中材料参数的统计特性分析[C]//2008 全国MTS断裂测试研讨会论文集, 2008: 26-31.
Qu X, Ma Y, Cui H. Statistical research of material coefficient in paris law[C]//Chinese MTS Fracture Test Symposium. 2008: 26-31.
[15] 曹俊, 袁慎芳, 蔡建, 等.疲劳裂纹扩展的实时健康监测[J].压电与声光, 2008, 30(6): 776-778.
Cao J, Yuan S F, Cai J, et al. Real-time structural health monitoring for fatigue crack growth[J]. Piezoelectrics and Acoustooptics, 2008, 30(6): 776-778.
[16] 袁慎芳, 邱雷, 王强, 等.压电-光纤综合结构健康监测系统的研究及验证[J].航空学报, 2009, 30(2): 348-356.
Yuan S F, Qiu L, Wang Q, et al. Application Research of a Hybrid Piezoelectric-optic Fiber Integrated Structural Health Monitoring System [J]. Acta Aeronautica Et Astronautica Sinica, 2009, 30(2): 348-356.
[17] 陆希,孟光,李富才.基于Lamb波的薄壁槽状结构损伤检测研究[J].振动与冲击, 2012, 31(12):63-67.
Lu X, Qiu L, Wang Q, et al. Lamb wave-based damage detection for a channel-like thin-wall structure [J]. Journal of Vibration and Shock, 2012, 31(12):63-67.
[18] 邱雷, 袁慎芳.集成压电健康监测扫查系统的研制及其应用[J].压电与声光, 2008, 30(1): 39-41.
Qiu L, Yuan S F. Research and application of integrated health monitoring scanning system based on PZT sensor array[J]. Piezoelectrics & Acoustooptics, 30(1): 39-41.
[19] Qiu Lei, Yuan Shenfang, Wang Qiang, et al. Design and experiment of PZT network-based structural health monitoring scanning system[J]. Chinese Journal of Aeronautics, 2009, 22(5): 505-512.
[20] Dong M, Wang N. Adaptive network-based fuzzy inference system with leave-one-out cross-validation approach for prediction of surface roughness[J]. Applied Mathematical Modelling, 2011, 35(3): 1024-1035.
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