Lead Zirconate Titanate-based bolt looseness monitoring using multiscale singular spectrum entropy analysis and genetic algorithm-based support vector machine
Restricted accessResearch articleFirst published online July, 2024
Lead Zirconate Titanate-based bolt looseness monitoring using multiscale singular spectrum entropy analysis and genetic algorithm-based support vector machine
The looseness monitoring of bolted joints is a significant issue to ensure structural integrity and safety in the industrial field. This paper proposes a novel approach to monitor bolt looseness based on piezoelectric active sensing. During the research, piezoelectric material is acted as an exciter to generate ultrasonic signals and a transducer is used to receive ultrasonic signals. In the process of signal processing, singular spectrum analysis (SSA) including phase reconstruction and principal component analysis is adopted to decompose the signal. Multiscale sample entropy (MSE) is employed to map the dynamic characteristics and regularity of the decomposed signals on multiple scales. The proposed strategy, named multiscale singular spectrum entropy analysis, refers to use MSE values of the new time series decomposed and reconstructed by SSA, to extract signal characteristics. Such a strategy can explore the underlying dynamical characteristics of a signal quantitatively in the reconstructed phase space. In our research work, SSA is employed to decompose the signals acquired by Lead Zirconate Titanate (PZT) to matrices, arranged from high to low singular values, and reconstruct the new time series (principal components) by diagonal averaging on determined matrices to characterize the essential dynamic characteristics of signals. MSE values of the principal components are used as damage index and adopted as input of genetic algorithm-based SVM to train a classifier to fulfill accurate monitoring of bolt joints. The theoretical derivation, application researches and comparison analysis can validate the effectiveness and superiority of the proposed approach in the field of bolt looseness monitoring.
ZhangZLiuMSuZ, et al.Quantitative evaluation of residual torque of a loose bolt based on wave energy dissipation and vibro-acoustic modulation: a comparative study. J Sound Vibration2016; 383: 156–170.
2.
YinTJiangQHYuenKV. Vibration-based damage detection for structural connections using incomplete modal data by Bayesian approach and model reduction technique. Eng Structures2017; 132: 260–277.
3.
LiQJingX. Fault diagnosis of bolt loosening in structures with a novel second-order output spectrum–based method. Structural Health Monitoring2020; 19(1): 123–141.
4.
NaWS. Bolt loosening detection using impedance based non-destructive method and probabilistic neural network technique with minimal training data. Eng Structures2021; 226: 111228.
5.
MinJParkSYunCB, et al. Impedance-based structural health monitoring incorporating neural network technique for identification of damage type and severity. Eng Structures2012; 39: 210–220.
6.
RamanaLChoiWChaYJ. Fully automated vision-based loosened bolt detection using the Viola–Jones algorithm. Structural Health Monitoring2019; 18(2): 422–434.
7.
ChaYJChenJGBüyüköztürkO. Output-only computer vision based damage detection using phase-based optical flow and unscented Kalman filters. Eng Structures2017; 132: 300–313.
8.
WangFHoSCMSongG. Modeling and analysis of an impact-acoustic method for bolt looseness identification. Mech Systems Signal Processing2019; 133: 106249.
9.
KudelaPRadzienskiMOstachowiczW. Impact induced damage assessment by means of Lamb wave image processing. Mech Systems Signal Processing2018; 102: 23–36.
10.
ChaYJChoiWBüyüköztürkO. Deep Learning-Based Crack Damage Detection Using Convolutional Neural Networks. Computer-Aided Civil Infrastructure Eng2017; 32(5): 361–378.
11.
KongQZhuJHoSCM, et al.. Tapping and listening: a new approach to bolt looseness monitoring. Smart Mater Struct2018; 27(7): 07LT02.
12.
WangFHuoLSongG. A piezoelectric active sensing method for quantitative monitoring of bolt loosening using energy dissipation caused by tangential damping based on the fractal contact theory. Smart Mater Struct2017; 27(1): 015023.
13.
SunXShaoJZhouY, et al. The method of bolt axial force looseness monitoring and control by piezoelectric ceramics in bolted joint structures. Proceedings Institution Mech Engineers, Part C: J Mech Eng Science2021; 236: 3277–3292.
14.
ZhangMShenYXiaoL, et al.Application of subharmonic resonance for the detection of bolted joint looseness. Nonlinear Dyn2017; 88(3): 1643–1653.
15.
HuoLWangFLiH, et al.A fractal contact theory based model for bolted connection looseness monitoring using piezoceramic transducers. Smart Mater Struct2017; 26(10): 104010.
16.
KongQRobertRHSilvaP, et al.Cyclic crack monitoring of a reinforced concrete column under simulated pseudo-dynamic loading using piezoceramic-based smart aggregates. Applied Sciences2016; 6: 341.
17.
JiQParvasiSMHoSCM, et al.Wireless energy harvesting using time reversal technique: an experimental study with numerical verification. J Intelligent Material Syst Structures2017; 28(19): 2705–2716.
18.
KongQHouSJiQ, et al.Very early age concrete hydration characterization monitoring using piezoceramic based smart aggregates. Smart Mater Struct2013; 22(8): 085025.
19.
LawWWLiaoWHHuangJ. Vibration control of structures with self-sensing piezoelectric actuators incorporating adaptive mechanisms. Smart Mater Struct2003; 12(5): 720–730.
20.
OstachowiczWKudelaPMalinowskiP, et al.Damage localisation in plate-like structures based on PZT sensors. Mech Systems Signal Processing2009; 23: 1805–1829.
21.
KangZFengCLiuZ, et al.Analysis of the incipient cavitation noise signal characteristics of hydroturbine. Applied Acoustics2017; 127: 118–125.
22.
AmiratYBenbouzidMEHWangT, et al.EEMD-based notch filter for induction machine bearing faults detection. Applied Acoustics2018; 133: 202–209.
23.
LeiYLiuZOuazriJ, et al.A fault diagnosis method of rolling element bearings based on CEEMDAN. Proceedings Institution Mech Engineers, Part C: J Mech Eng Science2017; 231(10): 1804–1815.
24.
YuanRLvYSongG. Multi-fault diagnosis of rolling bearings via adaptive projection intrinsically transformed multivariate empirical mode decomposition and high order singular value decomposition. Sensors2018; 18(4): 1210.
25.
LvYYuanRSongG. Multivariate empirical mode decomposition and its application to fault diagnosis of rolling bearing. Mech Systems Signal Processing2016; 81: 219–234.
26.
SunYQinNMaL. High-speed train bogie faults diagnosis using singular spectrum analysis. IEEE 7th Data Driven Control and Learning Systems Conference (DDCLS). IEEE2018: 56–59.
27.
LiHCuiLGuoS. A hybrid short-term power load forecasting model based on the singular spectrum analysis and autoregressive model. Advances in Electrical Eng2014; 2014: 1–7.
28.
MaraabaLSMemonAMAbidoMA, et al.An efficient acoustic-based diagnosis of inter-turn fault in interior mount LSPMSM. Applied Acoustics2021; 173: 107661.
29.
GolyandinaNNekrutkinVZhigljavskyAA. Analysis of time series structure: SSA and related techniques. Boca Raton, FL: CRC Press, 2001.
MuruganathamBSanjithMAKrishnakumarB, et al.Roller element bearing fault diagnosis using singular spectrum analysis. Mech Systems Signal Processing2013; 35: 150–166.
32.
RichmanJMoormanR. Physiological time series analysis using approximate entropy and sample entropy. Am J Physiol Heart Circ Physiol2000; 278(6): H2039–H2049.
33.
PincusSM. Approximate entropy as a measure of system complexity. Proc Natl Acad Sci U.S.A1991; 88: 2297–2301.
34.
HanMPanJ. A fault diagnosis method combined with LMD, sample entropy and energy ratio for roller bearings. Measurement2015; 76: 7–19.
35.
CostaMGoldbergerALPengCK. Multiscale entropy analysis of complex physiologic time series. Phys Rev Lett2002; 89: 068102.
36.
LvYYuanRWangT, et al.Health degradation monitoring and early fault diagnosis of a rolling bearing based on CEEMDAN and improved MMSE. Materials2018; 11(6): 1009.
37.
ZhangXChenXBarkhausPE, et al.Multiscale entropy analysis of different spontaneous motor unit discharge patterns. IEEE J Biomed Health Inform2013; 17(2): 470–476.
38.
ZhangLXiongGLiuH, et al.Applying improved multi-scale entropy and support vector machines for bearing health condition identification. Proceedings Institution Mech Engineers, Part C: J Mech Eng Science2010; 224(6): 1315–1325.
39.
YuanRLvYLiH, et al.Robust fault diagnosis of rolling bearings using multivariate intrinsic multiscale entropy analysis and neural network under varying operating conditions. IEEE Access2019; 7: 130804–130819.
40.
GolyandinaNKorobeynikovAZhigljavskyA. Singular spectrum analysis with R. New York: Springer, 2018.
41.
HuHGuoSLiuR, et al.An adaptive singular spectrum analysis method for extracting brain rhythms of electroencephalography. PeerJ2017; 5: e3474.
42.
GuanSWangXHuaL, et al.Quantitative ultrasonic testing for near-surface defects of large ring forgings using feature extraction and GA-SVM. Applied Acoustics2021; 173: 107714.
43.
XieJTowseyMZhangJ, et al.Acoustic classification of Australian frogs based on enhanced features and machine learning algorithms. Applied Acoustics2016; 113: 193–201.
44.
HuangYWuDZhangZ, et al.EMD-based pulsed TIG welding process porosity defect detection and defect diagnosis using GA-SVM. J Materials Processing Technology2017; 239: 92–102.
45.
VapnikV. The nature of statistical learning theory. Berlin, Germany: Springer Science & Business Media, 2013.
46.
MitchellM. An introduction to genetic algorithms. Cambridge, MA: MIT Press, 1998.
47.
MohammadiKShamshirbandSTongCW, et al.A new hybrid support vector machine-wavelet transform approach for estimation of horizontal global solar radiation. Energy Conversion Management2015; 92: 162–171.
48.
SolodovIY. Nonlinear NDE using contact acoustic nonlinearity (CAN). Proceedings IEEE Ultrasonics Symp1994; 2: 1279–1283, IEEE.
49.
BiwaSNakajimaSOhnoN. On the acoustic nonlinearity of solid-solid contact with pressure-dependent interface stiffness. J Applied Mechanics2004; 71(4): 508–515.
