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
  • [1] On particle filter improvements for on-line crack growth prognosis with guided wave monitoring
    Chen, Jian
    Yuan, Shenfang
    Wang, Hui
    Yang, Weibo
    SMART MATERIALS AND STRUCTURES, 2019, 28 (03)
  • [2] On-line prognosis of fatigue crack propagation based on Gaussian weight-mixture proposal particle filter
    Chen, Jian
    Yuan, Shenfang
    Qiu, Lei
    Wang, Hui
    Yang, Weibo
    ULTRASONICS, 2018, 82 : 134 - 144
  • [3] FATIGUE CRACK GROWTH PROGNOSIS WITH THE PARTICLE FILTER AND ON-LINE GUIDED WAVE STRUCTURAL MONITORING DATA
    Chen, Jian
    Yuan, Shenfang
    Qiu, Lei
    Ren, Yuanqiang
    PROCEEDINGS OF ASME 2021 INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION (IMECE2021), VOL 13, 2021,
  • [4] On-line updating Gaussian process measurement model for crack prognosis using the particle filter
    Chen, Jian
    Yuan, Shenfang
    Wang, Hui
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2020, 140
  • [5] Particle filter for fatigue crack growth prediction using SH0 wave on-line monitoring
    Li, Zhiwen
    Jia, Jiuhong
    Wang, Mingyuan
    Gu, Mengqi
    Tu, Shandong
    ULTRASONICS, 2024, 142
  • [6] Research on a Lamb Wave and Particle Filter-Based On-Line Crack Propagation Prognosis Method
    Chen, Jian
    Yuan, Shenfang
    Qiu, Lei
    Cai, Jian
    Yang, Weibo
    SENSORS, 2016, 16 (03):
  • [7] On-line crack prognosis in attachment lug using Lamb wave-deterministic resampling particle filter-based method
    Yuan, Shenfang
    Chen, Jian
    Yang, Weibo
    Qiu, Lei
    SMART MATERIALS AND STRUCTURES, 2017, 26 (08)
  • [8] Mixture Proposal Particle Filtering for Guided Wave Based Fatigue Crack Propagation Prognosis
    Chen, Jian
    Yuan, Shefang
    Wang, Hui
    STRUCTURAL HEALTH MONITORING - FROM SENSING TO DIAGNOSIS AND PROGNOSIS, 2017, 188 : 25 - 32
  • [9] On-Line Model-Based Prognosis for Crack Growth Under Variable Amplitude Loading
    Shin, Dae Han
    Leem, Sang Hyuck
    Choi, Joo-Ho
    PROCEEDINGS OF 2013 INTERNATIONAL CONFERENCE ON QUALITY, RELIABILITY, RISK, MAINTENANCE, AND SAFETY ENGINEERING (QR2MSE), VOLS I-IV, 2013, : 1683 - 1688
  • [10] On-line prognosis of fatigue cracking via a regularized particle filter and guided wave monitoring
    Chen, Jian
    Yuan, Shenfang
    Jin, Xin
    MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2019, 131 : 1 - 17