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
  • [21] Cardiac Activation Maps Reconstruction: A Comparative Study Between Data-Driven and Physics-Based Methods
    Karoui, Amel
    Bendahmane, Mostafa
    Zemzemi, Nejib
    [J]. FRONTIERS IN PHYSIOLOGY, 2021, 12
  • [22] A new model updating strategy with physics-based and data-driven models
    Xiang, Yongyong
    Pan, Baisong
    Luo, Luping
    [J]. STRUCTURAL AND MULTIDISCIPLINARY OPTIMIZATION, 2021, 64 (01) : 163 - 176
  • [23] A PHYSICS-BASED DATA-DRIVEN APPROACH FOR MODELING OF ENVIRONMENTAL DEGRADATION IN ELASTOMERS
    Ghaderi, Aref
    Chen, Yang
    Dargazany, Roozbeh
    [J]. PROCEEDINGS OF ASME 2022 INTERNATIONAL MECHANICAL ENGINEERING CONGRESS AND EXPOSITION, IMECE2022, VOL 9, 2022,
  • [24] Predicting Kinase Inhibitor Resistance: Physics-Based and Data-Driven Approaches
    Aldeghi, Matteo
    Gapsys, Vytautas
    de Groot, Bert L.
    [J]. ACS CENTRAL SCIENCE, 2019, 5 (08) : 1468 - 1474
  • [25] A deeper look into natural sciences with physics-based and data-driven measures
    Rodrigues, Davi Rohe
    Everschor-Sitte, Karin
    Gerber, Susanne
    Horenko, Illia
    [J]. ISCIENCE, 2021, 24 (03)
  • [26] Hybrid physics-based and data-driven impact localisation for composite laminates
    Xiao, Dong
    Sharif-Khodaei, Zahra
    Aliabadi, M. H.
    [J]. INTERNATIONAL JOURNAL OF MECHANICAL SCIENCES, 2024, 274
  • [27] A Data-Driven and Physics-Based Approach to Exploring Interdependency of Interconnected Infrastructure
    Zhou, Shenghua
    Ng, S. Thomas
    Yang, Yifan
    Xu, Frank Jun
    Li, Dezhi
    [J]. COMPUTING IN CIVIL ENGINEERING 2019: DATA, SENSING, AND ANALYTICS, 2019, : 82 - 88
  • [28] Distributed Planning of Collaborative Locomotion: A Physics-Based and Data-Driven Approach
    Fawcett, Randall T.
    Ames, Aaron D.
    Hamed, Kaveh Akbari
    [J]. IEEE ACCESS, 2023, 11 : 128369 - 128382
  • [29] 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
  • [30] Data-driven and physics-based approach for wave downscaling: A comparative study
    Juan, Nerea Portillo
    Rodriguez, Javier Olalde
    Valdecantos, Vicente Negro
    Iglesias, Gregorio
    [J]. OCEAN ENGINEERING, 2023, 285