Automatic Gauge Control Hydraulic Cylinder State Identification Using Modified Image Based Acoustic Emission Profile

Article Preview

Abstract:

The growing requirements of steel products qualities bring even tricky restrictions to the rolling process. With the fast response, reliable control and low maintenance requirements, the hydraulic automatic gauge control system has been widely applied to screw down the roller to maintain the precise rolling spaces for product quality control. Regarding to non-stopped heavy duties charged to the system, unavoidable faults and disfunctions not only influence the product quality, but also bring underlying safety issues. The hydraulic cylinder is the executing component of the roller screw down and it has the dominant percentage of hydraulic components faults. Working with unexpect loading is one of major impact factor that causes several chain-effects happened to the cylinder, as the classical diagnostic process is lack of cross-validating and time-consuming, the paper proposes the potentials of using acoustic emission to fill the dilemmas. The works include the data acquisition process to record the ultrasound acoustic signals from the hydraulic cylinder during it was loaded with 6 types of conditions, a modified image based acoustic emission approach constructed by using 8 significant waveform features was applied to generate visual effects and transform the cylinder acoustic emission signals under various loadings to a uniform format, the subtle differences among various loadings can be observed based on the pixel and intensity changes of the images. By applying the principal component analysis to project the acoustic emission image profiles onto the 3D plane, a clear trajectory can be observed with normal and overload conditions allocated upon the positive and negative sides of the axis. The result provided not only the potential of using acoustic emission for dynamic state identification of the subtle changes, but also opens up the possibility of preventive measures to the cylinder at risks in the future.

You might also be interested in these eBooks

Info:

Periodical:

Pages:

788-795

Citation:

Online since:

August 2013

Export:

Price:

Permissions CCC:

Permissions PLS:

Сopyright:

© 2013 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

[1] A. Stepanov, S. Zinchenko, S. Efimov, V. Ordin, M. Filatov, et al. Main trends in the growth of converter steelmaking at the company severstal, Metallurgist, Vol. 49(9/10), 2005, pp.380-2, doi: 10. 1007/s11015-006-0010-0.

DOI: 10.1007/s11015-006-0010-0

Google Scholar

[2] H. Liu. Application of predicted extrapolation control strategy in hot strip rolling mills gauge system, Proc. World Automat Cong (WAC), Jun, 2012, pp.1-4.

Google Scholar

[3] H. Wang, Y. Rong, J. Cui, and S. Liu, Study on knowledge processing of fault diagnosis for hydraulic AGC system, 2nd IEEE Intl Conf Infor Man Eng (ICIME), Apr, 2010, pp.1-3, doi: 10. 1109/icime. 2010. 5477802.

DOI: 10.1109/icime.2010.5477802

Google Scholar

[4] H. Wang, Y. Rong, S. Liu, and J. Cui, Identification for hydraulic AGC system of strip mill based on neural networks, Intl Conf Comput Desgn Appl (ICCDA), Jun, 2010, pp. V2-377-80, doi: 10. 1109/iccda. 2010. 5541406.

DOI: 10.1109/iccda.2010.5541406

Google Scholar

[5] M. Dong, C. Liu, and G. Li, Robust fault diagnosis based on nonlinear model of hydraulic gauge control system on rolling mill, IEEE Trans Contr Syst Tech, vol. 18(2), 2010, pp.510-5, doi: 10. 1109/tcst. 2009. 2019750.

DOI: 10.1109/tcst.2009.2019750

Google Scholar

[6] X. Wang, C. Liu, and M. Li, Sensors fault diagnosis of hydraulic automatic gauge control system based on wavelet neural network, IEEE Intl Conf Electr Contr Eng (ICECE), Jun, 2010, pp.3009-12, doi: 10. 1109/iCECE. 2010. 732.

DOI: 10.1109/icece.2010.732

Google Scholar

[7] X. Wang and K. Zhang, Sensors fault diagnosis of hydraulic automatic gauge control system based on neural network optimized by genetic algorithm, IEEE Intl Conf Oxide Mater Electr Eng (OMEE), Sept, 2012, pp.114-7.

DOI: 10.1109/omee.2012.6343516

Google Scholar

[8] W. M. Ye, Z. Xu, and K. Deng, The fault and diagnosis for hydraulic system of metallurgical machinery, China High Tech Enterp, Dec, 2008, pp.124-125. (Chinese).

Google Scholar

[9] P. Garimella and B. Yao, Model based fault detection of an electro-hydraulic cylinder, Proc. Amer Contr Conf, Vol. 1, Jun, 2005, pp.484-9, doi: 10. 1109/acc. 2005. 1469982.

DOI: 10.1109/acc.2005.1469982

Google Scholar

[10] L. Zhang, C. Zhang, and T. Shi, Inner leakage fault diagnosis of hydraulic cylinder using wavelet energy, Adv Mater Res, vol. 139-141, 2010, pp.2517-21, doi: 10. 4028/www. scientific. net/AMR. 139-141. 2517.

DOI: 10.4028/www.scientific.net/amr.139-141.2517

Google Scholar

[11] S. Qian and D. Chen, Joint time frequency analysis: methods and applications, Prentice-Hall, (1996).

Google Scholar

[12] C. M. Bishop, Neural networks for pattern recognition, Clarendon Presss, (1995).

Google Scholar

[13] H. Chen, Discovery of acoustic emission based biomarker for quantitative assessment of knee joint ageing and degeneration, in computing, engineering and physical science. Ph. D Thesis, Preston: University of Central Lancashire, (2011).

Google Scholar

[14] C. K. Tan, P. Irving, and D. Mba, A comparative experimental study on the diagnostic and prognostic capabilities of acoustics emission, vibration and spectrometric oil analysis for spur gears, Mech Syst Signal Pr, vol. 21(1), 2007, pp.208-33.

DOI: 10.1016/j.ymssp.2005.09.015

Google Scholar

[15] ASTM Standard E650/650M, 2012, Standard guide for mounting piezoelectric acoustic emission sensors, ASTM International, West Conshohocken, PA, 2012, doi: 10. 1520/E0650_E0650M-12, www. astm. org.

Google Scholar

[16] ASTM Standard E1930 / E1930M, 2012, Standard practice for examination of liquid-filled atmospheric and low-pressure metal storage tanks using acoustic emission, ASTM International, West Conshohocken, PA, 2012, doi: 10. 1520/E1930_E1930M-12, www. astm. org.

DOI: 10.1520/e1930_e1930m-17

Google Scholar

[17] B. Mascaro, J. Prior, L. K. Shark, J. Selfe, P. Cole, et al. Exploratory study of a non-invasive method based on acoustic emission for assessing the dynamic integrity of knee joints, Med Eng Phy, vol. 31(8), 2009, pp.1013-22.

DOI: 10.1016/j.medengphy.2009.06.007

Google Scholar

[18] L. K. Shark, H. Chen, and J. Goodacre, Discovering differences in acoustic emission between healthy and osteoarthritic knees using a four-phase model of sit-stand-sit movements, J Open Med Infor, vol. 4, 2010, pp.116-25.

DOI: 10.2174/1874431101004010116

Google Scholar

[19] L. K. Shark, H. Chen, and J. Goodacre, Knee acoustic emission: A clue to joint ageing and failure, Rheumatology, 49, I79, 2010, doi: 10. 1093/rheumatology/keq722.

Google Scholar

[20] L. K. Shark, H. Chen, and J. Goodacre, Knee acoustic emission: A potential biomarker for quantitative assessment of joint ageing and degeneration, Med Eng Phy, 33(5), 2011, 534-45, doi: 10. 1016/j. medengphy. 2010. 12. 009.

DOI: 10.1016/j.medengphy.2010.12.009

Google Scholar

[21] H. Chen, Y. Lu, L. Wang, Analysis of dynamic acoustic emission signals using multivariate statistical technique for smaller dataset, J Vibr Meas Diag, 2013, in press (Chinese).

Google Scholar

[22] I.T. Jolliffe, Principal component analysis, Springer-Verlag, (1986).

Google Scholar

[23] V. Salinas, Y. Vargas, J. Ruzzante, and L. Gaete, Localization algorithm for acoustic emission, Phys Procedia, vol. 3(1), 2010, pp.863-71.

DOI: 10.1016/j.phpro.2010.01.111

Google Scholar