Fault Detection of Yarn Congestion in Sizing Machine Based on Machine Vision

Abstract

During the sizing process, yarn congestion fault occurs at the reed teeth of a sizing machine. At present, the yarn congestion fault is generally handled by manual detection. The sizing production line operates on a large scale and runs continuously. Untimely handling of the yarn congestion fault causes a large amount of yarn waste. In this research, a machine vision-based algorithm for yarn congestion fault detection is developed. Through the analysis of the congestion fault and interference contour characteristics, the basic idea of image phase subtraction to identify the congestion fault is determined. To address the interference information appearing after image phase subtraction, the image pre-processing methods of Canny edge extraction and mean filtering are employed. According to the fault size and location characteristics, the fault contour detection algorithm based on inter-frame difference is designed. To mitigate the camera vibration interference, the anti-vibration interference algorithm based on affine transformation is studied, and the fault detection algorithm for the total yarn congestion fault is determined. The detection of 20 sets of field data is carried out, and the detection rate reaches 90%. This fault detection algorithm realizes the automatic detection of yarn congestion fault of sizing machine with certain real-time performance and accuracy.

Keywords

machine vision / yarn congestion / fault detection / inter-frame difference / affine transformation

References

[1] XIAO H B. Technological development and outlook of sizing machine[J]. China Textile Leader, 2013(2):54,56-57. (in Chinese)

[2] YANG Z Q. Modernization of sizing engineering technology[J]. Advanced Textile Technology, 2002(3):12-17. (in Chinese)

[3] ZHOU J, PAN R R, GAO W D. Application status and development trend of machine vision in textile[J]. Cotton Textile Technology, 2019, 47(2):15-17. (in Chinese)

[4] ZENG K. Research on mobile robot obstacle avoidance based on optical flow method[D]. Tangshan: North China University of Science and Technology, 2017. (in Chinese)

[5] SCHNEIDER D, GLOY Y S, MERHOF D. Vision-based on-loom measurement of yarn densities in w oven fabrics[J]. IEEE Transactions on Instrumentation and Measurement, 2015, 64(4):1063-1074

[6] SHI P F, BAI R L, YANG W H, et al. Broken yarn detection system on w arping machine based on machine vision[J]. Journal of Donghua University (Natural Science), 2011, 37 (6):750-754,760. (in Chinese)

[7] ZHANG J L, LI Q. Online cheese package yarn density detection system based on machine vision[J]. Journal of Textile Research, 2020, 41(6):141-146. (in Chinese)

[8] HALEEM N, BUSTREO M, DEL BUE A. Acomputer vision based online quality control system for textile yarns[J]. Computers in Industry, 2021,133:103550.

[9] XU C Q, WANG J L, TAO J, et al. Aknow ledge augmented deep learning method for vision-based yarn contour detection[J]. Journal of Manufacturing Systems, 2022,63:317-328.

[10] DLAMINI S, KAO C Y, SU S L, et al. Development of a real-time machine vision system for functional textile fabric defect detection using a deep YOLOv4 model[J]. Textile Research Journal, 2022, 92(5/6):675-690.

[11] ZUO B, WANG F L. Surface cutting defect detection of magnet using Fourier image reconstruction[J]. Computer Engineering and Applications, 2016, 52 (3):256-260,265. (in Chinese)

[12] KHANAL S R, SILVA J, GONZALEZ D G, et al. Fabric hairiness analysis for quality inspection of pile fabric products using computer vision technology[J]. Procedia Computer Science, 2022,204:591-598.

[13] GÜLTEKⅰN E, ÇELⅰK Hⅰ, DÜLGER L C, et al. Image processing applications on yarn characteristics and fault inspection[J]. Tekstil Ve Mühendis, 2019, 26(116):340-345.

[14] MUSA E. Line-laser-based yarn shadow sensing break sensor[J]. Optics and Lasers in Engineering, 2011, 49(3):313-317.

[15] XIA X M, MENG S, PAN R R, et al. On-line detection of w arp collision and reed embedding based on improved inter-frame difference method[J]. Journal of Textile Research, 2021, 42(6):91-96. (in Chinese)

[16] LI C Z, WEI K H, ZHAO Y B, et al. Improvement of high-speed detection algorithm for nonw oven material defects based on machine vision[J]. Journal of Donghua University(English Edition), 2024, 41(4):416-427.

[17] GAO W D, JIANG W M, WANG J G, et al. Technological development history and prospect of domestic sizing machine[J]. Cotton Textile Technology, 2023, 51 (10):41-46. (in Chinese)

Funding

National Key Research and Development Program of China(2017YFB1304001)