On particle filter improvements for on-line crack growth prognosis with guided wave monitoring

被引:5
作者
Chen, Jian [1 ]
Yuan, Shenfang [1 ]
Wang, Hui [1 ]
Yang, Weibo [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, Jiangsu, Peoples R China
基金
中国国家自然科学基金;
关键词
on-line prognosis; fatigue crack growth; improved particle filter; structural health monitoring; guided wave; FATIGUE; PROPAGATION; DIAGNOSIS; FAILURE; LUGS;
D O I
10.1088/1361-665X/aaf93e
中图分类号
TH7 [仪器、仪表];
学科分类号
0804 ; 080401 ; 081102 ;
摘要
Accurate prognosis of fatigue crack growth is of great importance to ensure structural integrity, which is a challenging task due to various uncertainties affecting crack growth. To deal with this problem, the particle filter (PF) based prognostics that incorporates on-line structural health monitoring (SHM) becomes a new trend. However, most existing studies adopt the basic PF algorithm, which needs improvements to meet the requirement for on-line prognosis. It refers to the choice of the importance density and the resampling strategy, as well as the definition of the measurement equation that correlates SHM data to crack states. Till now, no literature addresses this topic in-depth Aiming at on-line crack growth prognosis, this paper combines four improved PFs with the guided wave based SHM. The study is carried out under two cases respectively, which involve whether or not the measurement equation is accurately trained based on fatigue test data of a kind of aircraft attachment lug. Not only prognostic accuracy and consistency, but also effects of the particle number on the performance and computational cost are analyzed. The result shows advantages and disadvantages of each improved PF for on-line crack growth prognosis, giving instructions to choose appropriate PFs for different application scenarios.
引用
收藏
页数:22
相关论文
共 30 条
[1]   FATIGUE CRACK GROWTH PROGNOSIS WITH THE PARTICLE FILTER AND ON-LINE GUIDED WAVE STRUCTURAL MONITORING DATA [J].
Chen, Jian ;
Yuan, Shenfang ;
Qiu, Lei ;
Ren, Yuanqiang .
PROCEEDINGS OF ASME 2021 INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION (IMECE2021), VOL 13, 2021,
[2]   Dual crack growth prognosis by using a mixture proposal particle filter and on-line crack monitoring [J].
Chen, Jian ;
Yuan, Shenfang ;
Sbarufatti, Claudio ;
Jin, Xin .
RELIABILITY ENGINEERING & SYSTEM SAFETY, 2021, 215
[3]   On-line prognosis of fatigue cracking via a regularized particle filter and guided wave monitoring [J].
Chen, Jian ;
Yuan, Shenfang ;
Jin, Xin .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2019, 131 :1-17
[4]   On-line updating Gaussian process measurement model for crack prognosis using the particle filter [J].
Chen, Jian ;
Yuan, Shenfang ;
Wang, Hui .
MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2020, 140
[5]   On-line crack prognosis in attachment lug using Lamb wave-deterministic resampling particle filter-based method [J].
Yuan, Shenfang ;
Chen, Jian ;
Yang, Weibo ;
Qiu, Lei .
SMART MATERIALS AND STRUCTURES, 2017, 26 (08)
[6]   On-line prognosis of fatigue crack propagation based on Gaussian weight-mixture proposal particle filter [J].
Chen, Jian ;
Yuan, Shenfang ;
Qiu, Lei ;
Wang, Hui ;
Yang, Weibo .
ULTRASONICS, 2018, 82 :134-144
[7]   A PZT Based On-Line Updated Guided Wave-Gaussian Process Method for Crack Evaluation [J].
Yuan, Shenfang ;
Wang, Hui ;
Chen, Jian .
IEEE SENSORS JOURNAL, 2020, 20 (15) :8204-8212
[8]   Guided wave-hidden Markov model for on-line crack evaluation of a full-scale aircraft [J].
Zhang J. ;
Yuan S. ;
Chen J. .
Aerospace Science and Technology, 2021, 117
[9]   Mixture Proposal Particle Filtering for Guided Wave Based Fatigue Crack Propagation Prognosis [J].
Chen, Jian ;
Yuan, Shefang ;
Wang, Hui .
STRUCTURAL HEALTH MONITORING - FROM SENSING TO DIAGNOSIS AND PROGNOSIS, 2017, 188 :25-32
[10]   Lamb Wave-Minimum Sampling Variance Particle Filter-Based Fatigue Crack Prognosis [J].
Yang, Weibo ;
Gao, Peiwei .
SENSORS, 2019, 19 (05)