Restricted accessResearch articleFirst published online 2025
Noncontact tension detection method for carbon fiber precursor filaments based on laser Doppler vibrometer and optimized variational mode decomposition
Tension detection during the spinning process of carbon fiber precursor filaments plays a crucial role in enhancing the quality of carbon fibers. The noncontact tension detection method is proposed for carbon fiber precursor filaments in this paper. Based on Hamilton’s principle, the dynamic differential equations of the ribbonlike filament bundle are established, and a general formula relating filament bundle tension to vibration frequency is derived. In terms of signal processing methods, the Improved Aquila Optimization algorithm is first used to optimize the variational mode decomposition parameters, including the number of modes K and the penalty factor α, to decompose the signal into components of different frequencies. Subsequently, a combined screening method using the Pearson correlation coefficient, amplitude-sensitive arrangement entropy, and DBSCAN is employed to identify and remove noise signals, thereby achieving signal denoising. Experimental results indicate that this method for screening signal components achieves better results compared with the single correlation coefficient screening method. The measurement errors of laser-Doppler-vibrometer-based tension detection are within ±11.5%. This method enables noncontact and nondestructive tension detection, providing a new solution for situations where traditional contact-based detection is not feasible. Therefore, this method has significant research potential.
TianW. Study on the construction and formation mechanism of polar surface structure of high modulus carbon fibers. Master, Beijing University of Chemical Technology, 2023. DOI: 10.26939/d.cnki.gbhgu.2023.001313
2.
QinQXuLChenT, et al. Preparation technology of high-performance polyacrylonitrile based carbon fiber precursors. Synth Fiber China2024; 53: 45-49.
3.
ZhaoHLiCRenR, et al. Effect of drafting environment on orientation structure of polyacrylonitrile precursor. New Chem Mater2014; 42: 124-126.
4.
WangHHouYHeY, et al. A physical-constrained decomposition method of infrared thermography: Pseudo restored heat flux approach based on ensemble Bayesian variance tensor fraction. IEEE Trans Ind Informat2024; 20: 3413-3424. DOI: 10.1109/tii.2023.3293863.
5.
WangYHuiYChenX, et al. Perforated piezoresistive film-based flexible bidirectional strain sensors for large bending deformation detection and health monitoring of glass fiber-reinforced polymers. Compos B Eng2025; 293. DOI: 10.1016/j.compositesb.2025.112111.
6.
ChengSZhangHChenX, et al. Electric-assisted coaxial wet spinning of radially oriented boron nitride nanosheet-based composite fiber with highly enhanced piezoelectricity. Adv Fiber Mater2025; 7: 1302-1316. DOI: 10.1007/s42765-025-00567-0.
7.
ZhangSZhouJZhangH, et al. Influence of cable tension history on the monitoring of cable tension using magnetoelastic inductance method. Struct Health Monitor2021; 20: 3392-3405. DOI: 10.1177/1475921720987987.
8.
WangXChenMSunH, et al. Relationship of cable tension and temperature based on long-term monitoring data on the cable-stayed bridge. In: 2nd International Conference on Civil, Architectural and Hydraulic Engineering (ICCAHE 2013)Zhuhai, Peoples’ Republic of China, 2013. Jul 27-28 2013, Progress in industrial and civil engineering ii, pts 1-4, pp.1716-1721.
9.
SunXXiaoHMengW.Research on the tension of steel cord conveyor belts based on transverse vibration modelling. Meas Sci Technol2024; 35. DOI: 10.1088/1361-6501/ad180d.
10.
HomanTSekiKIwasakiM, et al. Tension estimation and control in winding process of web handling systems. In: 30th IEEE International Symposium on Industrial Electronics (ISIE), Kyoto, Japan, 2021.
11.
ZhengBJiangG.Numerical simulation and tension regulation of yarn mechanics on warp knitting machines. Text Res J2023; 93: 817-833. DOI: 10.1177/00405175221125945.
12.
JanaDNagarajaiahSYangY, et al. Real-time cable tension estimation from acceleration measurements using wireless sensors with packet data losses: analytics with compressive sensing and sparse component analysis. J Civ Struct Health Monitor2022; 12: 797-815. DOI: 10.1007/s13349-021-00526-4.
13.
XiongQGaoX.Current situation and outlook of yarn tension measurement and control technology. Cotton Text Technol2011; 39: 405-408.
14.
ZhangDGanXYangC, et al. Research progress in non-contact fiber tension detection technology in spinning process. Journal of Textile Research2022; 43: 188-194. DOI: 10.13475/j.fzxb.20210403607.
15.
ChenCWangCLvW, et al. Research progress on yarn tension sensors. Adv Text Technol2022; 30: 32-41. DOI: 10.19398/j.att.202111050.
16.
JiYMaJZhouZ, et al. Dynamic yarn-tension detection using machine vision combined with a tension observer. Sensors2023; 23. DOI: 10.3390/s23083800.
17.
MiaoYMengXXiaG, et al. Research and development of non-contact yarn tension monitoring system. Wool Text J2020; 48: 71-76. DOI: 10.19333/j.mfkj.20190903406.
18.
ZhangYPengLXuY, et al. Research on non-contact yarn state detection technology. Adv Text Technol2022; 30: 101-108. DOI: 10.19398/j.att.202012020.
19.
PengLJiangJ.Design of yarn tension measurement system based on image processing. Softw Eng2023; 26: 11-15. DOI: 10.19644/j.cnki.issn2096-1472.2023.011.003.
20.
VedrinesMGassmannVKnittelD.Moving web-tension determination by out-of-plane vibration measurements using a laser. IEEE Trans Instrum Meas2009; 58: 207-213. DOI: 10.1109/tim.2008.928408.
ZhangDMaQTanY, et al. Non-contact detection of polyester filament yarn tension in the spinning process by the laser Doppler vibrometer method. Text Res J2022; 92: 919-928. DOI: 10.1177/00405175211034246.
23.
HuangNEShenZLongSR, et al. The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proc R Soc A Math Phys Eng Sci1998; 454: 903-995. DOI: 10.1098/rspa.1998.0193.
24.
DragomiretskiyKZossoD.Variational mode decomposition. IEEE Trans Signal Process2014; 62: 531-544. DOI: 10.1109/tsp.2013.2288675.
25.
CuiHGuanYDengW.Fault diagnosis using cascaded adaptive second-order tristable stochastic resonance and empirical mode decomposition. Appl Sci Basel2021; 11. DOI: 10.3390/app112311480.
26.
WangWDongYLiuF, et al. A VMD-BP model to predict laser welding keyhole-induced pore defect in Al butt-lap joint. Materials2024; 17. DOI: 10.3390/ma17133270.
27.
ZhuYJiaYWangL, et al. Feature extraction and classification on partial discharge signals of power transformers based on improved variational mode decomposition and Hilbert transform. Trans China Electrotech Soc2017; 32: 221-235.
28.
BanksHTHuSKenzZR.A brief review of elasticity and viscoelasticity for solids. Adv Appl Math Mech2011; 3: 1-51. DOI: 10.4208/aamm.10-m1030.
29.
LiSGaoJXiaoB, et al. Fractal analysis of dimensionless permeability and Kozeny-Carman constant of spherical granular porous media with randomly distributed tree-like branching networks. Phys Fluids2024; 36(6): 063614. DOI: 10.1063/5.0218990.
30.
TuBXiaoBZhangY, et al. An analytical model for permeability of fractal tree-like branched networks composed of converging-diverging capillaries. Phys Fluids2024; 36(4): 043621. DOI: 10.1063/5.0201040.
31.
XiaoBHuangQChenH, et al. A fractal model for capillary flow through a single tortuous capillary with roughened surfaces in fibrous porous media. Fractals Complex Geom Patterns Scaling Nat Soc2021; 29(1): 2150017. DOI: 10.1142/s0218348x21500171.
32.
XiaoBZhuHChenF, et al. A fractal analytical model for Kozeny-Carman constant and permeability of roughened porous media composed of particles and converging-diverging capillaries. Powder Technol2023; 420: 118256. DOI: 10.1016/j.powtec.2023.118256.
33.
DjordjevicIMSekulicDRStevanovicMM.Non-linear elastic behaviour of carbon fibres of different structural and mechanical characteristic. J Serb Chem Soc2007; 72: 513-521. DOI: 10.2298/jsc0705513d.
34.
BaiYGongXLuQ, et al. Adaptive vibration control method for doublecrystal monochromator base on VMD and FxNLMS. J Synch Rad2023; 30: 308-318. DOI: 10.1107/s1600577523000528.
35.
LiYTangBJiangX, et al. Bearing fault feature extraction method based on GA-VMD and center frequency. Math Prob Eng2022; 2022. DOI: 10.1155/2022/2058258.
36.
MaoMZengKTanZ, et al. Adaptive VMD-K-SVD-based rolling bearing fault signal enhancement study. Sensors2023; 23. DOI: 10.3390/s23208629.
37.
ChenZLiYLiangH, et al. Improved permutation entropy for measuring complexity of time series under noisy condition. Complexity2019. DOI: 10.1155/2019/1403829.
38.
AzamiHEscuderoJ.Amplitude-aware permutation entropy: Illustration in spike detection and signal segmentation. Comput Meth Prog Biomed2016; 128: 40-51. DOI: 10.1016/j.cmpb.2016.02.008.
