Acoustic fault signal extraction via the line-defect phononic crystals

Tinggui CHEN , Bo WU , Dejie YU

Front. Mech. Eng. ›› 2022, Vol. 17 ›› Issue (1) : 10

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Front. Mech. Eng. ›› 2022, Vol. 17 ›› Issue (1) : 10 DOI: 10.1007/s11465-021-0666-y
RESEARCH ARTICLE
RESEARCH ARTICLE

Acoustic fault signal extraction via the line-defect phononic crystals

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Abstract

Rotating machine fault signal extraction becomes increasingly important in practical engineering applications. However, fault signals with low signal-to-noise ratios (SNRs) are difficult to extract, especially at the early stage of fault diagnosis. In this paper, 2D line-defect phononic crystals (PCs) consisting of periodic acrylic tubes with slit are proposed for weak signal detection. The defect band, namely, the formed resonance band of line-defect PCs enables the incident acoustic wave at the resonance frequency to be trapped and enhanced at the resonance cavity. The noise can be filtered by the band gap. As a result, fault signals with high SNRs can be obtained for fault feature extraction. The effectiveness of weak harmonic and periodic impulse signal detection via line-defect PCs are investigated in numerical and experimental studies. All the numerical and experimental results indicate that line-defect PCs can be well used for extracting weak harmonic and periodic impulse signals. This work will provide potential for extracting weak signals in many practical engineering applications.

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phononic crystals / line-defect / fault signal extraction / acoustic enhancement

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Tinggui CHEN, Bo WU, Dejie YU. Acoustic fault signal extraction via the line-defect phononic crystals. Front. Mech. Eng., 2022, 17(1): 10 DOI:10.1007/s11465-021-0666-y

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References

[1]

PalmaP, SteigerR. Structural health monitoring of timber structures—review of available methods and case studies. Construction and Building Materials, 2020, 248 : 118528

[2]

GardnerP, LiuX, WordenK. On the application of domain adaptation in structural health monitoring. Mechanical Systems and Signal Processing, 2020, 138 : 106550

[3]

LiX, Cheng J, ShaoH D, LiuK, Cai B P. A fusion CWSMM-based framework for rotating machinery fault diagnosis under strong interference and imbalanced case. IEEE Transactions on Industrial Informatics, 2021 (in press)

[4]

ZhangD C, StewartE, EntezamiM, RobertsC, YuD J. Intelligent acoustic-based fault diagnosis of roller bearings using a deep graph convolutional network. Measurement, 2020, 156 : 107585

[5]

KotP, MuradovM, GkantouM, KamarisG S, HashimK, YeboahD. Recent advancements in non-destructive testing techniques for structural health monitoring. Applied Sciences (Basel, Switzerland), 2021, 11( 6): 2750

[6]

ShaoH D, XiaM, Wan J F, De SilvaC. Modified stacked auto-encoder using adaptive morlet wavelet for intelligent fault diagnosis of rotating machinery. IEEE/ASME Transactions on Mechatronics, 2022, 27(1): 24− 33

[7]

KontoudisG P, KraussS, StilwellD J. Model-based learning of underwater acoustic communication performance for marine robots. Robotics and Autonomous Systems, 2021, 142 : 103811

[8]

DanaweH, OkudanG, OzevinD, TolS. Conformal gradient-index phononic crystal lens for ultrasonic wave focusing in pipe-like structures. Applied Physics Letters, 2020, 117( 2): 021906

[9]

HadA, SabriK. A two-stage blind deconvolution strategy for bearing fault vibration signals. Mechanical Systems and Signal Processing, 2019, 134 : 106307

[10]

ZhangD C, EntezamiM, StewartE, RobertsC, YuD J, LeiY G. Wayside acoustic detection of train bearings based on an enhanced spline-kernelled chirplet transform. Journal of Sound and Vibration, 2020, 480 : 115401

[11]

KhareS K, BajajV, SinhaG R. Adaptive tunable Q wavelet transform-based emotion identification. IEEE Transactions on Instrumentation and Measurement, 2020, 69( 12): 9609– 9617

[12]

XuL, ChattertonS, PennacchiP. Rolling element bearing diagnosis based on singular value decomposition and composite squared envelope spectrum. Mechanical Systems and Signal Processing, 2021, 148 : 107174

[13]

QiaoZ J, LeiY G, LiN P. Applications of stochastic resonance to machinery fault detection: a review and tutorial. Mechanical Systems and Signal Processing, 2019, 122 : 502– 536

[14]

LuS L, HeQ B, WangJ. A review of stochastic resonance in rotating machine fault detection. Mechanical Systems and Signal Processing, 2019, 116 : 230– 260

[15]

HyunJ, ChoW H, ParkC S, ChangJ H, KimM. Achromatic acoustic gradient-index phononic crystal lens for broadband focusing. Applied Physics Letters, 2020, 116( 23): 234102

[16]

NassarH, ChenY Y, HuangG L. Isotropic polar solids for conformal transformation elasticity and cloaking. Journal of the Mechanics and Physics of Solids, 2019, 129 : 229– 243

[17]

ColombiA, RouxP, GuenneauS, RupinM. Directional cloaking of flexural waves in a plate with a locally resonant metamaterial. The Journal of the Acoustical Society of America, 2015, 137( 4): 1783– 1789

[18]

LauretiS, HutchinsD A, AstolfiL, WatsonR L, ThomasP J, BurrascanoP, NieL, FreearS, AskariM, ClareA T, RicciM. Trapped air metamaterial concept for ultrasonic sub-wavelength imaging in water. Scientific Reports, 2020, 10( 1): 10601

[19]

BoccaccioM, RachigliaP, Malfense FierroG P, Pio PucilloG, MeoM. Deep-subwavelength-optimized holey-structured metamaterial lens for nonlinear air-coupled ultrasonic imaging. Sensors (Basel), 2021, 21( 4): 1170

[20]

LeeT, NomuraT, SuX S, IizukaH. Fano-like acoustic resonance for subwavelength directional sensing: 0–360 degree measurement. Advanced Science, 2020, 7( 6): 1903101

[21]

ColombiA, AgeevaV, SmithR J, ClareA, PatelR, ClarkM, ColquittD, RouxP, GuenneauS, CrasterR V. Enhanced sensing and conversion of ultrasonic rayleigh waves by elastic metasurfaces. Scientific Reports, 2017, 7( 1): 6750

[22]

MiniaciM, PalR K, MannaR, RuzzeneM. Valley-based splitting of topologically protected helical waves in elastic plates. Physical Review B, 2019, 100( 2): 024304

[23]

JiangT X, HeQ B, PengZ K. Enhanced directional acoustic sensing with phononic crystal cavity resonance. Applied Physics Letters, 2018, 112( 26): 261902

[24]

JoS H, YoonH, ShinY C, YounB D. An analytical model of a phononic crystal with a piezoelectric defect for energy harvesting using an electroelastically coupled transfer matrix. International Journal of Mechanical Sciences, 2021, 193 : 106160

[25]

LiY G, ChenT N, WangX P, MaT, JiangP. Acoustic confinement and waveguiding in two-dimensional phononic crystals with material defect states. Journal of Applied Physics, 2014, 116( 2): 024904

[26]

ShakeriA, DarbariS, Moravvej-FarshiM K. Designing a tunable acoustic resonator based on defect modes, stimulated by selectively biased PZT rods in a 2D phononic crystal. Ultrasonics, 2019, 92 : 8– 12

[27]

DavisB L, HusseinM I. Thermal characterization of nanoscale phononic crystals using supercell lattice dynamics. AIP Advances, 2011, 1( 4): 041701

[28]

Villa-ArangoS, TorresR, KyriacouP A, LucklumR. Fully-disposable multilayered phononic crystal liquid sensor with symmetry reduction and a resonant cavity. Measurement, 2017, 102 : 20– 25

[29]

NingS W, YangF Y, LuoC C, LiuZ L, ZhuangZ. Low-frequency tunable locally resonant band gaps in acoustic metamaterials through large deformation. Extreme Mechanics Letters, 2020, 35 : 100623

[30]

LiY, WuY, MeiJ. Double Dirac cones in phononic crystals. Applied Physics Letters, 2014, 105( 1): 014107

[31]

ZhangD C, EntezamiM, StewartE, RobertsC, YuD J. Adaptive fault feature extraction from wayside acoustic signals from train bearings. Journal of Sound and Vibration, 2018, 425 : 221– 238

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