Dual crack growth prognosis by using a mixture proposal particle filter and on-line crack monitoring

被引:22
|
作者
Chen, Jian [1 ]
Yuan, Shenfang [1 ]
Sbarufatti, Claudio [2 ]
Jin, Xin [1 ]
机构
[1] Nanjing Univ Aeronaut & Astronaut, Res Ctr Struct Hlth Monitoring & Prognosis, State Key Lab Mech & Control Mech Struct, 29 Yudao St, Nanjing 210016, Peoples R China
[2] Politecn Milan, Dept Mech Engn, Via La Masa 1, I-20156 Milan, Italy
基金
中国国家自然科学基金;
关键词
On-line prognosis; Dual crack growth; Guided wave; Structural health monitoring; Mixture proposal particle filter; ARTIFICIAL NEURAL-NETWORKS; DATA-DRIVEN; PROPAGATION; QUANTIFICATION; DIAGNOSIS; TUTORIAL; SYSTEM; MODEL;
D O I
10.1016/j.ress.2021.107758
中图分类号
T [工业技术];
学科分类号
08 ;
摘要
On-line prognosis of fatigue cracks in the structure is challenging due to various uncertainties affecting fatigue crack initiation and growth. This paper proposes an on-line prognosis strategy for fatigue cracks by incorporating the mixture proposal particle filter (MPPF) and structural health monitoring (SHM) results. In this method, a dynamic crack evolution model is proposed to deal with the situation that more than one crack occurs and grows in the structure. Meanwhile, crack sizes monitored by the SHM technique are incorporated to construct an effective mixture proposal of the importance probability density, which is the key for sampling new particles. Further, posterior estimations of the fatigue crack sizes and the crack evolution model parameters are evaluated with these particles, based on which the prognosis of fatigue crack growth is carried out. A leave-one-out validation is performed on the dual crack growth problem of the hole-edge-cracked structure, demonstrating the effectiveness of the proposed method.
引用
收藏
页数:14
相关论文
共 50 条
  • [21] Operational characteristics of filter separators based on on-line particle monitoring experiments
    Zheng S.
    Luo M.
    Han H.
    Bie Q.
    Liu Y.
    Li Q.
    Chen S.
    Zhu N.
    Mao Y.
    Liu Z.
    Natural Gas Industry, 2020, 40 (05) : 109 - 116
  • [22] On-line fatigue crack closure determination using nonlinear ultrasound testing
    Jia J.
    Tao L.
    Hu H.
    Hu Y.
    Lyu W.
    Tao, Limin (tlm1964@sina.com), 2018, National University of Defense Technology (40): : 97 - 102
  • [23] A particle interaction-based crack model using an improved smoothed particle hydrodynamics for fatigue crack growth simulations
    Wiragunarsa, I. Made
    Zuhal, Lavi Rizki
    Dirgantara, Tatacipta
    Putra, Ichsan Setya
    INTERNATIONAL JOURNAL OF FRACTURE, 2021, 229 (02) : 229 - 244
  • [24] A particle interaction-based crack model using an improved smoothed particle hydrodynamics for fatigue crack growth simulations
    I Made Wiragunarsa
    Lavi Rizki Zuhal
    Tatacipta Dirgantara
    Ichsan Setya Putra
    International Journal of Fracture, 2021, 229 : 229 - 244
  • [25] On-line passenger estimation in a metro system using particle filter
    Reyes, Francisco
    Cipriano, Aldo
    IET INTELLIGENT TRANSPORT SYSTEMS, 2014, 8 (01) : 1 - 8
  • [26] Monitoring of fatigue crack growth using guided ultrasonic waves
    Masserey, B.
    Kostson, E.
    Fromme, P.
    SENSORS AND SMART STRUCTURES TECHNOLOGIES FOR CIVIL, MECHANICAL, AND AEROSPACE SYSTEMS 2010, 2010, 7647
  • [27] Monitoring fatigue crack growth and opening using antenna sensors
    Mohammad, I.
    Huang, H.
    SMART MATERIALS AND STRUCTURES, 2010, 19 (05)
  • [28] Probabilistic Prognosis of Fatigue Crack Growth Using Acoustic Emission Data
    Zarate, Boris A.
    Caicedo, Juan M.
    Yu, Jianguo
    Ziehl, Paul
    JOURNAL OF ENGINEERING MECHANICS, 2012, 138 (09) : 1101 - 1111
  • [29] Crack growth evaluation based on the extended finite element and particle filter combined method
    Xie, Guizhong
    Li, Jinghui
    Li, Hao
    Wang, Liangwen
    Li, Xiaoke
    Geng, Hongrui
    ENGINEERING ANALYSIS WITH BOUNDARY ELEMENTS, 2024, 169
  • [30] Lamb Wave-Minimum Sampling Variance Particle Filter-Based Fatigue Crack Prognosis
    Yang, Weibo
    Gao, Peiwei
    SENSORS, 2019, 19 (05)