Prognostic Diagnosis of Hollow Ball Screw Pretension on Preload Loss Through Sensed Vibration Signals

  • Yi-Cheng Huang
  • Yu-Shi Chen
  • Shi-Lun Sun
  • Kuan-Heng Peng
Conference paper
Part of the Lecture Notes in Electrical Engineering book series (LNEE, volume 234)


The pretension for a ball screw is a way to improve the position accuracy. Hollow ball screw without a cooling system has the thermal deformation effect due to increase in temperature. It will reduce precision accuracy in machine tool when the ball screw nut preload or ball screw pretension is lost. The purpose of this study is to use vibration signals for the prognostic analysis for the ball screw pretension. Features of different pretension conditions by 0, 5, 10, and 20μm are discriminated by empirical mode decomposition (EMD), fast Fourier transform (FFT), and marginal frequency method. Temperature effects with long-term operation were discussed. This study experimentally extracts the characteristic frequencies for bettering pretension through the vibration signals. This diagnosis results realize the purpose of prognostic effectiveness on knowing the hollow ball screw preload loss based on pretension data and utilizing convenience.


Hollow ball screw Vibration signals Hilbert–Huang transform FFT 



This work is supported in part by NSC 101-2221-E-018-010 and NSC 97-EC-17-A-05-S1-101. The authors are much appreciated.


  1. 1.
    Denkena B, Harms A, Jacobsen J, Möhring H-C, Lange D, Noske H (2006) Life-cycle Oriented Development of Machine Tools. In: 13th cooperative institutional research program international conference on life cycle engineering, pp 693–698Google Scholar
  2. 2.
    Huang NE, Shen SSP (2005) Hilbert–Huang transform and its applications. World Scientific Publishing, SingaporeMATHGoogle Scholar
  3. 3.
    Huang NE, Shen Z, Long SR, Wu MC, Shih HH, Zheng Q et al (1998) The empirical mode decomposition and the Hilbert spectrum for nonlinear and nonstationary time series analysis. Proc R Soc A 454:903–995MathSciNetMATHCrossRefGoogle Scholar
  4. 4.
    Peng ZK, Tse PW, Chu FL (2005) A comparison study of improved Hilbert–Huang transform and wavelet transform application to fault diagnosis for rolling bearing. Mech Syst Signal Process 19:974–988CrossRefGoogle Scholar
  5. 5.
    Costa M, Goldberger AL, Peng C-K (2005) Multiscale entropy analysis of biological signals. Phys Rev Express 71:021906-1–021906-18MathSciNetGoogle Scholar
  6. 6.
    Antonino-Daviu J, Jover Rodriguez P, Riera-Guasp A, Arkkio M, Roger-Folch J, Perez RB (2009) Transient detection of eccentricity-related components in induction motors through the Hilbert–Huang transform. Energy Convers Manage 50:1810–1820CrossRefGoogle Scholar
  7. 7.
    Huang YC, Shin YC (2012) Method of intelligent fault diagnosis of preload loss for single nut ball screws through the sensed vibration signals. In: International conference on machine learning and data analysis, 65, World Academy of Science, Engineering and Technology, Tokyo, Japan, pp 1394–1401Google Scholar

Copyright information

© Springer Science+Business Media New York 2013

Authors and Affiliations

  • Yi-Cheng Huang
    • 1
  • Yu-Shi Chen
    • 1
  • Shi-Lun Sun
    • 1
  • Kuan-Heng Peng
    • 1
  1. 1.Department of Mechatronics EngineeringNational Changhua University of EducationChanghuaTaiwan

Personalised recommendations