Roll eccentricity compensation based on anti-aliasing wavelet analysis method

  • Zhi-ming ChenEmail author
  • Fei Luo
  • Yu-ge Xu
  • Wei Yu


Roll eccentricity is an important factor causing thickness variations during hot strip rolling and might define the limit of strip thickness control accuracy. An improved multi-resolution wavelet transform algorithm was proposed to compensate for the roll eccentricity. The wavelet transform method had good localization characteristics in both the time and frequency domains for signal analysis; however, the wavelet method had a frequency-aliasing problem owing to the less than ideal cut-off frequency characteristics of wavelets. This made its component reconstruction of an inaccurate signal. To eliminate inherent frequency aliases in the wavelet transform, fast Fourier transform (FFT) and inverse fast Fourier transform (IFFT) were combined with the Mallat algorithm. This synthesis was described in detail. Then, the roll eccentricity component was extracted from rolling force signal. An automatic gauge control (AGC) system added with a multi-resolution wavelet analyzer was designed. Experimental results showed that the anti-aliasing method could greatly restrain the inverse effect of eccentricity and the thickness control accuracy was improved from ±40 μm to ±15 μm.

Key words

hot strip rolling roll eccentricity compensation wavelet analysis frequency alias 


Unable to display preview. Download preview PDF.

Unable to display preview. Download preview PDF.


  1. 1.
    Aistleitner K, Mattersdorfer L G, Haas W, et al. Neural Network for Identification of Roll Eccentricity in Rolling Mills [J]. Journal of Materials Processing Technology, 1996, 60(1–4): 387.CrossRefGoogle Scholar
  2. 2.
    WANG Wei-ren, WANG Zheng-lin, SUN Yi-kang. Application of Multi-Resolution Wavelet Controller in Rolling Eccentricity Control [J]. Journal of University of Science and Technology Beijing, 2005, 27(6): 728 (in Chinese).Google Scholar
  3. 3.
    LI Bo-qun. Application and Research of Synthetical AGC System in Hot Rolling Mill [D]. Beijing: University of Science and Technology Beijing, 2006 (in Chinese).Google Scholar
  4. 4.
    HUANG Min, FANG Xiao-ke, WANG Jian-hui, et al. Roll Eccentricity Compensation Control for Strip Rolling Mills Based on Wavelet Packet De-Noising Theory [A]. Institute of Electrical and Electronics Engineers Inc, eds. Proceedings of the 5th WCICA [C]. Piscataway: Institute of Electrical and Electronics Engineers Inc, 2004. 3565.Google Scholar
  5. 5.
    LI Yong, WANG Jun, HU Xian-shuo, et al. AGC System With Adaptive Threshold Method of Wavelet Transform [J]. Iron and Steel, 2007, 42(1): 39 (in Chinese).Google Scholar
  6. 6.
    Mallat S G. A Theory for Multi-Resolution Signal Decomposition: The Wavelet Representation [J]. IEEE Transactions on Pattern Analysis and Machine Intelligence, 1989, 11(7): 674.CrossRefGoogle Scholar
  7. 7.
    YANG Jian-guo. Wavelet Analysis and Its Engineering Applications [M]. Beijing: China Machine Press, 2005 (in Chinese).Google Scholar
  8. 8.
    YANG Jian-guo, Park S T. An Anti-Aliasing Algorithm for Discrete Wavelet Transform [J]. Mechanical System and Signal Processing, 2003, 17(5): 945.CrossRefGoogle Scholar

Copyright information

© China Iron and Steel Research Institute Group 2009

Authors and Affiliations

  1. 1.College of Automation Science and EngineeringSouth China University of TechnologyGuangzhou, GuangdongChina

Personalised recommendations