Review of piezoelectric impedance based structural health monitoring: Physics-based and data-driven methods

被引:0
|
作者
Fan, Xingyu [1 ,2 ]
Li, Jun [1 ,3 ]
Hao, Hong [3 ]
机构
[1] Guangzhou Univ, Guangzhou Univ Curtin Univ Joint Res Ctr Struct M, Sch Civil Engn, Guangzhou, Peoples R China
[2] Xian Univ Technol, Sch Mech & Precis Instrument Engn, Xian, Peoples R China
[3] Curtin Univ, Sch Civil & Mech Engn, Ctr Infrastruct Monitoring & Protect, Bentley, WA, Australia
关键词
piezoelectric materials; electromechanical impedance technique; non-destructive testing; data-driven method; physics-based method; damage quantification; COUPLED ELECTROMECHANICAL ANALYSIS; INTELLIGENT MATERIAL SYSTEMS; ACTUATOR POWER-CONSUMPTION; ADAPTIVE MATERIAL SYSTEMS; DAMAGE IDENTIFICATION; WAVE-PROPAGATION; PIEZOCERAMIC ELEMENTS; PRACTICAL ISSUES; FREQUENCY-DOMAIN; ACTIVE-SENSORS;
D O I
10.1177/13694332211038444
中图分类号
TU [建筑科学];
学科分类号
0813 ;
摘要
Vibration based structural health monitoring methods are usually dependent on the first several orders of modal information, such as natural frequencies, mode shapes and the related derived features. These information are usually in a low frequency range. These global vibration characteristics may not be sufficiently sensitive to minor structural damage. The alternative non-destructive testing method using piezoelectric transducers, called as electromechanical impedance (EMI) technique, has been developed for more than two decades. Numerous studies on the EMI based structural health monitoring have been carried out based on representing impedance signatures in frequency domain by statistical indicators, which can be used for damage detection. On the other hand, damage quantification and localization remain a great challenge for EMI based methods. Physics-based EMI methods have been developed for quantifying the structural damage, by using the impedance responses and an accurate numerical model. This article provides a comprehensive review of the exciting researches and sorts out these approaches into two categories: data-driven based and physics-based EMI techniques. The merits and limitations of these methods are discussed. In addition, practical issues and research gaps for EMI based structural health monitoring methods are summarized.
引用
收藏
页码:3609 / 3626
页数:18
相关论文
共 50 条
  • [31] Physics-based modeling and data-driven algorithm for prediction and diagnosis of atherosclerosis
    Bahloul, Mohamed
    Belkhatir, Zehor
    Aboelkassem, Yasser
    Laleg-Kirati, Meriem T.
    [J]. BIOPHYSICAL JOURNAL, 2022, 121 (03) : 419A - 420A
  • [32] A new model updating strategy with physics-based and data-driven models
    Yongyong Xiang
    Baisong Pan
    Luping Luo
    [J]. Structural and Multidisciplinary Optimization, 2021, 64 : 163 - 176
  • [33] Design of a Physics-Based and Data-Driven Hybrid Model for Predictive Maintenance
    Traini, Emiliano
    Bruno, Giulia
    Lombardi, Franco
    [J]. ADVANCES IN PRODUCTION MANAGEMENT SYSTEMS: ARTIFICIAL INTELLIGENCE FOR SUSTAINABLE AND RESILIENT PRODUCTION SYSTEMS, PT V, 2021, 634 : 536 - 543
  • [34] Structural health monitoring system based on digital twins and real-time data-driven methods
    Li, Xiao
    Zhang, Feng-Liang
    Xiang, Wei
    Liu, Wei-Xiang
    Fu, Sheng-Jie
    [J]. Structures, 2024, 70
  • [35] A Review of the Piezoelectric Electromechanical Impedance Based Structural Health Monitoring Technique for Engineering Structures
    Na, Wongi S.
    Baek, Jongdae
    [J]. SENSORS, 2018, 18 (05)
  • [36] Estimation of elastic bandgaps in metastructures: A comparison of physics-based and data-driven approaches
    Gosavi, Hrishikesh
    Malladi, Vijaya V. N. Sriram
    [J]. MECHANICAL SYSTEMS AND SIGNAL PROCESSING, 2023, 201
  • [37] Integration of data-driven and physics-based modeling of wind waves in a shallow estuary
    Wang, Nan
    Chen, Qin
    Zhu, Ling
    Sun, Hao
    [J]. OCEAN MODELLING, 2022, 172
  • [38] Hybrid data-driven and physics-based modeling for viscosity prediction of ionic liquids
    Fan J.
    Dai Z.
    Cao J.
    Mu L.
    Ji X.
    Lu X.
    [J]. Green Energy and Environment, 2024, 9 (12): : 1878 - 1890
  • [39] Data-driven models for vessel motion prediction and the benefits of physics-based information
    Schirmann, Matthew L.
    Collette, Matthew D.
    Gose, James W.
    [J]. APPLIED OCEAN RESEARCH, 2022, 120
  • [40] Data-driven and physics-based reliability tests to failure of a power electronics converter
    Martino, Edoardo
    Fairbrother, Andrew
    Ghosh, Riddhi
    Schuderer, Jurgen
    [J]. MICROELECTRONICS RELIABILITY, 2023, 150