Fault Detection in the Two-for-One Twister

  • Park, Ho-Cheol (Department of Chemical Engineering, Kyungpook National University) ;
  • Koo, Doe-Gyoon (Department of Chemical Engineering, Kyungpook National University) ;
  • Lee, Jie-Tae (Department of Chemical Engineering, Kyungpook National University) ;
  • Cho, Hyun-Ju (Department of Home Economics Education, Kyungpook National University) ;
  • Han, Young-A (Department of Textile System Engineering, Kyungpook National University) ;
  • Sohn, Sung-Ok (Department of Textile System Engineering, Kyungpook National University) ;
  • Ji, Byung-Chul (Department of Textile System Engineering, Kyungpook National University)
  • Published : 2006.12.30

Abstract

The two-for-one(TFO) twister is precision machinery that twists fibers rapidly under constant tension. Since the quality of the twisted yarn is directly deteriorated by faults of the twister, such as the distortion of the spinning axis, bearing abrasion, and tension irregularity, it is important to detect faults of the TFO twister at an early stage. In this research, a new algorithm is proposed to detect faults of the TFO twister and their causes, by measuring the vibrations of the TFO twister and obtaining frequency components with a FFT algorithm. The TFO twister with faults showed increased vibrations and each fault generated vibrations at different frequencies. By analyzing changes of characteristics of vibrations, we can determine faulty twisters. The proposed fault detection algorithm can be implemented cheaply with a signal processor chip. It can be used to find when to repair a faulty TFO twister without much loss of yam on-line.

Keywords

References

  1. G. W. Du and J. W. S. Hearle, 'Mechanics of friction twisting, part I: Yam path, tension, and torque generation in a single disc,' Textile Res. J., vol. 61, no. 5, pp. 289-297, 1991 https://doi.org/10.1177/004051759106100508
  2. G. W. Du and J. W. S. Hearle, 'Mechanics of friction twisting, part II: Application of the single disc model to a triple-stack multi-disc spindle,' Textile Res. J., vol. 61, no. 6, pp. 347357, 1991 https://doi.org/10.1177/004051759106100606
  3. S. Ribolzi, I. Merckle, J. Gresser, and P. E. Exbrayat, 'Real-time fault detection on textiles using opto-electronic processing,' Textile Res. J., vol. 63, no. 2, pp. 61-71, 1993 https://doi.org/10.1177/004051759306300201
  4. X. Hong, H. Qiu, Y. Li, and C. Li, 'Online test and fault diagnosis of yam quality using wavelet analysis and FFT,' J. Dong Hua Univ., vol. 19, no. 2,pp. 99-103, 2002
  5. S. Sette and L. Boullart, 'Fault detection and quality assessment in textiles by means of neural nets,' International Journal of Clothing Science and Technology, vol. 8, no. 1/2, pp. 73-83, 1996 https://doi.org/10.1108/09556229610109627
  6. S. Prabhakar, A. R. Mohanty and A. S. Sekhar, 'Application of discrete wavelet transform for detection of ball bearing race faults,' Tribology International, vol. 35, no. 12, pp. 793-800,2002 https://doi.org/10.1016/S0301-679X(02)00063-4
  7. N. Baydar and A. Ball, 'Detection of gear failures via vibration and acoustic signals using wavelet transform,' Mechanical Systems and Signal Processing, vol. 17, no. 4, pp. 787-804, 2003 https://doi.org/10.1006/mssp.2001.1435
  8. W. H. Press, B. P. Flannery, S. A. Teukolsky, and w. T. Vetterling, Numerical Recipes in C, Cambridge University Press, 1990