The electromechanical impedance (EMI)-based damage identification method is a non-destructive testing approach in the field of structural health monitoring. The frequency response function (FRF) of EMI can effectively reveal the health conditions of a structure. Typically, the health condition is identified by comparing the FRF of a structure to that of a baseline. However, baselines may exhibit unpredictable shifts in real applications. In this study, a new EMI-based health identification method is proposed without reference to baselines or handcrafted features. An adjustable inductive shunt circuit that can enrich the EMI dataset is connected to a piezoelectric transducer. Pre-set damage, including bolt looseness and mass variations, are selected to demonstrate damage identification. The FRFs are extracted using a phase-sensitive detection algorithm. The damage identification model is realized using a one-dimensional convolutional neural network. Experimental results show that the proposed method can identify the location of bolt loosening and mass variation with an overall accuracy of 99.24%. The proposed method can be applied for identifying the health conditions of a structure with strong nonlinearity.
DasSSahaPPatroSK. Vibration-based damage detection techniques used for health monitoring of structures: a review. J Civ Struct Health Monit2016; 6: 477–507.
2.
KoduruJPRoseJL. Transducer arrays for omnidirectional guided wave mode control in plate like structures. Smart Mater Struct2013; 22(1): 015010.
3.
GorginRLuoYWuZ. Environmental and operational conditions effects on lamb wave based structural health monitoring systems: a review. Ultrasonics2020; 105: 106114.
4.
KimJWangKW. An enhanced impedance-based damage identification method using adaptive piezoelectric circuitry. Smart Mater Struct2014; 23(9): 095041.
5.
BudoyaDEBaptistaFG. A comparative study of impedance measurement techniques for structural health monitoring applications. IEEE Trans Instrum Meas2018; 67: 912–924.
6.
NaWBaekJ. A review of the piezoelectric electromechanical impedance based structural health monitoring technique for engineering structures. Sensors2019; 18: 1307.
7.
LiangCSunFRogersCA. Electro-mechanical impedance modeling of active material systems. Smart Mater Struct1996; 5: 171186.
8.
MartowiczASendeckiASalamonM, et al.Application of electromechanical impedance-based SHM for damage detection in bolted pipeline connection. Nondestruct Test Eval2016; 31: 17–44.
9.
Silva De FreitasEGuimarães BaptistaF. Experimental analysis of the feasibility of low-cost piezoelectric diaphragms in impedance-based SHM applications. Sensors Actuators A Phys2016; 238: 220–228.
10.
AnnamdasVGMRadhikaMA. Electromechanical impedance of piezoelectric transducers for monitoring metallic and non-metallic structures: a review of wired, wireless and energy-harvesting methods. J Intell Mater Syst Struct2013; 24: 1021–1042.
11.
XuJDongJHLiHN, et al.Looseness monitoring of bolted spherical joint connection using electro-mechanical impedance technique and BP neural networks. Sensors2019; 19: 1906.
12.
LimYYSmithSTPadillaRV, et al.Monitoring of concrete curing using the electromechanical impedance technique: review and path forward. Struct Health Monit2021; 20(2): 604–636.
13.
LiangCSunFPRogersCA. Coupled electro-mechanical analysis of adaptive material systems-determination of the actuator power consumption and system energy transfer. J Intell Mater Syst Struct1997; 8: 335–343.
14.
EzzatAATangJDingY. A model-based calibration approach for structural fault diagnosis using piezoelectric impedance measurements and a finite element model. Struct Health Monit2020; 19: 1839–1855.
15.
BaptistaFGFilhoJV. A new impedance measurement system for PZT-based structural health monitoring. IEEE Trans Instrum Meas2009; 58: 3602–3608.
16.
CaoPQiSTangJ. Structural damage identification using piezoelectric impedance measurement with sparse inverse analysis. Smart Mater Struct2018; 27(3): 035020.
17.
ShuaiQZhouKZhouS, et al.Fault identification using piezoelectric impedance measurement and model-based intelligent inference with pre-screening. Smart Mater Struct2017; 26(4): 045007.
18.
OhT-KKimJLeeC, et al.Nondestructive concrete strength estimation based on electro-mechanical impedance with artificial neural network. J Adv Concr Technol2017; 15(3): 94–102.
19.
SuY-FHanGAmranA, et al.Instantaneous monitoring the early age properties of cementitious materials using PZT-based electromechanical impedance (EMI) technique. Constr Build Mater2019; 225: 340–347.
20.
MinJParkSYunC-B, et al.Impedance-based structural health monitoring incorporating neural network technique for identification of damage type and severity. Eng Struct2012; 39: 210–220.
21.
de OliveiraMMonteiroAVieira FilhoJ. A new structural health monitoring strategy based on PZT sensors and convolutional neural network. Sensors2018; 18: 2955.
22.
BandaraRPChanTHThambiratnamDP. Structural damage detection method using frequency response functions. Struct Health Monit2014; 13: 418–429.
23.
ZhaoRYanRChenZ, et al.Deep learning and its applications to machine health monitoring. Mech Syst Signal Process2019; 115: 213–237.
24.
WangHXuJYanR, et al.Using SDP representation and SE-CNN. IEEE Trans Instrum Meas2019; 69: 2377–2389.
25.
WangHXuJYanR, et al.Intelligent bearing fault diagnosis using multi-head attention-based CNN. Procedia Manuf2020; 49: 112–118.
26.
ShaoSMcAleerSYanR, et al.Highly accurate machine fault diagnosis using deep transfer learning. IEEE Trans Ind Inform2019; 15: 2446–2455.
27.
ShaoSYanRLuY, et al.DCNN-based multi-signal induction motor fault diagnosis. IEEE Trans Instrum Meas2020; 69: 2658–2669.
28.
WangXTangJ. Damage detection using piezoelectric admittance approach with inductive circuitry. J Intell Mater Syst Struct2010; 21: 667–676.
29.
JuBFangLWangQ, et al.General rules for the inductive characteristics of a piezoelectric structure and its integration with piezoelectric transformer for capacitive compensation. Smart Mater Struct2021; 30(4): 045018.
30.
BudoyaDECampeiroLMBaptistaFG. Sensitivity enhancement of piezoelectric transducers for impedance-based damage detection via a negative capacitance interface. IEEE Sens J2020; 20(23): 13892–13900.
31.
GrippJABGóesLCSHeussO, et al.An adaptive piezoelectric vibration absorber enhanced by a negative capacitance applied to a shell structure. Smart Mater Struct2015; 24(12): 125017.
32.
WangXTangJ. An enhanced piezoelectric impedance approach for damage detection with circuitry integration. Smart Mater Struct2010; 19(4): 045001.
33.
ZhangWLiCPengG, et al.A deep convolutional neural network with new training methods for bearing fault diagnosis under noisy environment and different working load. Mech Syst Signal Process2018; 100: 439–453.
34.
De FreitasESBaptistaFGBudoyaDE, et al.Equivalent circuit of piezoelectric diaphragms for impedance-based structural health monitoring applications. IEEE Sens J2017; 17: 5537–5546.
35.
RibollaELMRizzoPGulizziV. On the use of the electromechanical impedance technique for the assessment of dental implant stability: modeling and experimentation. J Intell Mater Syst Struct2015; 26: 2266–2280.
36.
ZhangXXuJYanR. A structural impedance measurement method by using polyvinylidene fluoride as actuator and sensor. Rev Sci Instrum2020; 91: 085111.
37.
HoshyarmaneshHGhodsiMKimM, et al.Temperature effects on electromechanical response of deposited piezoelectric sensors used in structural health monitoring of aerospace structures. Sensors2019; 19: 2805.
38.
VillaniLGGda SilvaSCunhaA, et al.Damage detection in an uncertain nonlinear beam based on stochastic volterra series: an experimental application. Mech Syst Signal Process2019; 128: 463–478.
39.
XuJZhangXYanR. Experimental demonstration of the wave attenuation capability of a piezoelectric metamaterial beam by using correlation for signal processing. J Appl Phys2020; 128: 174903.
40.
CasadeiFRuzzeneMDozioL, et al.Broadband vibration control through periodic arrays of resonant shunts: experimental investigation on plates. Smart Mater Struct2010; 19(1): 015002.
41.
De OliveiraMAAraujoNVSda SilvaRN, et al.Use of Savitzky-Golay filter for performances improvement of SHM systems based on neural networks and distributed PZT sensors. Sensors2018; 18: 152.
42.
JiangLJTangJWangKW. On the tuning of variable piezoelectric transducer circuitry network for structural damage identification. J Sound Vib2008; 309: 695–717.
43.
JuniorPOCConteSD'AddonaDM, et alDamage patterns recognition in dressing tools using PZT-based SHM and MLP networks. In: 12th Cirp Conference on Intelligent Computation in Manufacturing Engineering, Naples, Italy, 18–20 July 2018, paper no. 79, pp. 303–307.
44.
MousaviZEttefaghMMSadeghiMH, et al.Developing deep neural network for damage detection of beam-like structures using dynamic response based on FE model and real healthy state. Appl Acoust2020; 168: 107402.
45.
FanXLiJHaoH. Impedance resonant frequency sensitivity based structural damage identification with sparse regularization: experimental studies. Smart Mater Struct2019; 28(1): 015003.
46.
KooKYParkSLeeJJ, et al.Automated impedance-based structural health monitoring incorporating effective frequency shift for compensating temperature effects. J Intell Mater Syst Struct2009; 20(4): 367–377.
