Viscoelastic sandwich structure is widely used in mechanical equipment. However, therein viscoelastic material inevitably suffers from gradual aging. To keep equipment running safety, it is urgent to perform the aging state identification of viscoelastic sandwich structure by vibration response signal analysis. Nevertheless, the structural vibration response signal is non-stationary and its variation caused by the structural aging state change is very puny. The vibration-based structural aging state identification has become a challenging task. Therefore, a novel method based on variational mode decomposition (VMD) with parameter optimization by sparrow search algorithm (SSA), and deep belief network (DBN) is proposed for this task. To extract sensitive aging feature information, the structural vibration response signal is processed by optimized variational mode decomposition with sparrow search algorithm (SSA-VMD), and multiple permutation entropy (PE) features are extracted from the intrinsic mode functions (IMFs) for reflecting. To attain intelligent and desirable aging state identification, using the extracted PE features as input, DBN is introduced for identifying structural aging states. The proposed method is performed on a viscoelastic sandwich structure to validate its feasibility and efficiency, and is compared with the traditional methods. The results show that the proposed method can obtain a better aging state intelligent identification result and has a good application prospect.
MoitaJSAraújoALMota SoaresCM, et al. Finite element model for damping optimization of viscoelastic sandwich structures. Adv Eng Softw2013; 66: 34–39.
2.
ClaudeBDuigouLGiraultG, et al. Study of damped vibrations of a vibroacoustic interior problem with viscoelastic sandwich structure using a high order Newton solver. J Sound Vib2019; 462: 114947.
3.
HuangZQinZChuF.A compression shear mixed finite element model for vibration and damping analysis of viscoelastic sandwich structures. J Sandwich Struct Mater2019; 21(6): 1775–1798.
4.
WangYZhangZSQuJX, et al. Time-frequency distribution decomposition with applications to recognize the looseness state of the viscoelastic sandwich structure. Meas Sci Technol2016; 27: 75001.
5.
LediKSHamdaouiMRobinG, et al. An identification method for frequency dependent material properties of viscoelastic sandwich structures. J Sound Vib2018; 428: 13–25.
6.
FanWQiaoPZ.Vibration-based damage identification methods: a review and comparative study. Struct Health Moniort2011; 10: 83–111.
7.
UbertiniFComanducciGCavalagliN.Vibration-based structural health monitoring of a historic bell-tower using output-only measurements and multivariate statistical analysis. Struct Health Monit2016; 15: 438–457.
8.
YangYDornCManciniT, et al. Reference-free detection of minute, non-visible, damage using full-field, high-resolution mode shapes output-only identified from digital videos of structures. Struct Health Monit2018; 17: 514–531.
9.
QuJZhangZLuoX, et al. A novel method to aging state recognition of viscoelastic sandwich structures. Steel Compos Struct2016; 21: 1183–1210.
10.
ZhengJPanH.Mean-optimized mode decomposition: an improved EMD approach for non-stationary signal processing. ISA Trans2020; 106: 392–401.
11.
ChenSZhaoHZhangS, et al. Study of ultra-wideband fuze signal processing method based on wavelet transform. IET Radar Sonar Navig2014; 8(3): 167–172.
12.
ZhuangDLiuHZhengH, et al. The IBA-ISMO method for rolling bearing fault diagnosis based on VMD-sample entropy. Sensors2023; 23(2): 991.
13.
LiFLiRTianL, et al. Data-driven time-frequency analysis method based on variational mode decomposition and its application to gear fault diagnosis in variable working conditions. Mech Syst Signal Process2019; 116: 462–479.
14.
GuoYHoYKZhaoX, et al. An IGSA-VMD method for fault frequency identification of cylindrical roller bearing. Proc Inst Mech Eng C J Mech Eng Sci2024; 238(18): 9307–9320.
15.
YuJZhaoKGuoZ, et al. RUL prediction of rolling bearings based on adaptive variational mode decomposition and double-discriminator conditional CycleGAN. Proc Inst Mech Eng C J Mech Eng Sci2024; 238(12): 6007–6017.
16.
ZhengXWangSQianY.Fault feature extraction of wind turbine gearbox under variable speed based on improved adaptive variational mode decomposition. Proc Inst Mech Eng J Power Energy2020; 234(6): 848–861.
17.
DingJHuangLXiaoD, et al. GMPSO-VMD algorithm and its application to rolling bearing fault feature extraction. Sensors2020; 20(7): 1946.
18.
MengZWangXLiuJ, et al. An adaptive spectrum segmentation-based optimized VMD method and its application in rolling bearing fault diagnosis. Meas Sci Technol2022; 33(12): 125107.
FanYZhangYGuoB, et al. A hybrid sparrow search algorithm of the hyperparameter optimization in deep learning. Mathematics2022; 10(16): 3019.
21.
LiLZhangZXuB.Prediction of spherical sheet springback based on a sparrow-search-algorithm-optimized BP neural network. Metals2022; 12(8): 1377.
22.
WangZHuangXZhuD.A multistrategy-integrated learning sparrow search algorithm and optimization of engineering problems. Comput Intell Neurosci2022; 2022: 1–21.
23.
LvJSunWWangH, et al. Coordinated approach fusing RCMDE and Sparrow search algorithm-based SVM for fault diagnosis of rolling bearings. Sensors2021; 21(16): 5297.
24.
ZhuJLiOChenM, et al. Fault diagnosis of wind turbine rolling bearings based on DCS-EEMD-SSA. J Fail Anal Prev2024; 24(5): 2495–2508.
25.
HouSZhengJPanH, et al. Multivariate multi-scale cross-fuzzy entropy and SSA-SVM-based fault diagnosis method of gearbox. Meas Sci Technol2024; 35(5): 056102.
26.
ZhangMXuCLiL, et al. Optimization of PID controller for stepper motor speed control system based on improved sparrow search algorithm. Proc Inst Mech Eng C J Mech Eng Sci2024; 238(19): 9397–9411.
27.
LiuXZhangZMengF, et al. Fault diagnosis of wind turbine bearings based on CNN and SSA–ELM. J Vib Eng Technol2023; 11(8): 3929–3945.
28.
LiuBCaiJ.A new fault diagnosis of rolling bearing using RCMPNDE and SSA-PNN. Ind Lubrication Tribol2025; 77(3): 419–428.
29.
ZhengJChenYPanH, et al. Composite multi-scale phase reverse permutation entropy and its application to fault diagnosis of rolling bearing. Nonlinear Dyn2023; 111: 459–479.
30.
XuFWai Tat TsePFangYJ, et al. A fault diagnosis method combined with compound multiscale permutation entropy and particle swarm optimization–support vector machine for roller bearings diagnosis. Proc Inst Mech Eng J J Eng Tribol2019; 233(4): 615–627.
31.
WangXSiSLiY, et al. An integrated method based on refined composite multivariate hierarchical permutation entropy and random forest and its application in rotating machinery. J Vib Control2020; 26: 146–160.
32.
KeYYaoCSongE, et al. Intelligent fault diagnosis method for common rail injectors based on hierarchical weighted permutation entropy and pair-wise feature proximity feature selection. J Vib Control2022; 28: 2386–2398.
33.
TaibiATouatiSAomarL, et al. Bearing fault diagnosis of induction machines using VMD-DWT and composite multiscale weighted permutation entropy. COMPEL2024; 43(3): 649–668.
34.
MaPLiangWZhangH, et al. Multiscale permutation entropy based on natural visibility graph and its application to rolling bearing fault diagnosis. Struct Health Monit2025; 24(1): 313–326.
YeQLiuC.A multichannel data fusion method based on multiple deep belief networks for intelligent fault diagnosis of main reducer. Symmetry2020; 12(3): 483.
37.
DashPBNaikBNayakJ, et al. Deep belief network-based probabilistic generative model for detection of robotic manipulator failure execution. Soft Comput2023; 27: 363–375.
38.
ShangZLiaoXGengR, et al. Fault diagnosis method of rolling bearing based on deep belief network. J Mech Sci Technol2018; 32(11): 5139–5145.
39.
JiangHShaoHChenX, et al. A feature fusion deep belief network method for intelligent fault diagnosis of rotating machinery. J Intell Fuzzy Syst2018; 34(6): 3513–3521.
40.
LvDWangHCheC.Fault diagnosis of rolling bearing based on multimodal data fusion and deep belief network. Proc Inst Mech Eng C J Mech Eng Sci2021; 235(22): 6577–6585.
41.
ShahMBoradeHDaveV, et al. Utilizing TGAN and ConSinGAN for improved tool wear prediction: a comparative study with ED-LSTM, GRU, and CNN models. Electronics2024; 13(17): 3484.
