Username   Password       Forgot your password?  Forgot your username? 

 

Degradation Index Extraction and Degradation Trend Prediction for Rolling Bearing

Volume 15, Number 5, May 2019, pp. 1334-1342
DOI: 10.23940/ijpe.19.05.p9.13341342

Xin Zhanga,*, Jianmin Zhaoa, Xianglong Nib, Haiping Lia, and Fucheng Sunb

aArmy Engineering University, Shijiazhuang, 050003, China
bElectronic Equipment Test Centre of China, Luoyang, 471003, China

 

(Submitted on December 8, 2018; Revised on January 12, 2019; Accepted on February 10, 2019)

Abstract:

In the degradation process of the rolling bearing, the traditional feature trends appear steady in the early stage and then show a sudden change trend in the later stage. For this reason, it is difficult to predict the bearing degradation trend accurately. In order to solve this problem, this paper puts forward a new degradation index extraction method based on dual-tree complex wavelet transform (DTCWT) and isometric feature mapping (ISOMAP). Compared with the traditional characteristic parameters, the new degradation index can better reflect the degradation tendency of the bearing. Considering the data of different time points has different contributions to degradation trend prediction, the improved BP neural network is applied to predict the degradation trend of bearing. The method is verified by using the bearing degradation data.

 

References: 13

    1. M. Unal, Y. Sahin, M. Onat, M. Denetgul, and H. Kucuk, “Fault Diagnosis of Rolling Bearings using Data Mining Techniques and Boosting,” Journal of Dynamic Systems, Measurement, and Control, Vol. 139, pp. 1-10, February 2017
    2. A. Purarjomandlangrudi, A. H. Ghapanchi, and M. Esmalifalak “A Data Mining Approach for Fault Diagnosis: An Application of Anomaly Detection Algorithm,” Measurement, Vol. 55, pp. 343-352, 2014
    3. E. Downham, “Vibration Monitoring and Wear Prediction,” in Proceeding of 2nd International Conference on Vibration in Rotary Machinery, pp. 29-33, 1980
    4. Y. Li, S. Billington, and C. Zhang, “Adaptive Prognostics for Rolling Element Bearing Condition,” Mechanical Systems and Signal Processing, Vol. 13, pp. 103-113, 1999
    5. Y. Li, T. R. Kurfess, and Y. Liang, “Stochastics Prognostics for Rolling Element Bearings,” Mechanical Systems and Signal Processing, Vol. 14, pp. 747-762, 2000
    6. E. Momeni, R. Nazir, and D. J. Armaghani, “Prediction of Pile Bearing Capacity using a Hybrid Genetic Algorithm based ANN,” Measurement, Vol. 57, pp. 122-131, 2014
    7. R. Huang, L. Xi, and X. Li, “Residual Life Prediction for Ball Bearings based on Self-Organizing Map and Back Propagation Neural Network Methods,” Mechanical Systems and Signal Processing, Vol. 21, pp. 193-207, 2007
    8. N. G. Kingsbury, “The Dual-Tree Complex Wavelet Transform: A New Technique for Shift Invariance and Directional Filters,” Digital Signal Processing Workshop, Vol. 98, pp. 2-5, 1998
    9. I. W. Selesnick, R. G. Baraniuk, and N. G. Kingsbury, “The Dual-Tree Complex Wavelet Transform,” IEEE Signal Processing Magazine, Vol. 22, pp. 123-151, 2005
    10. T. Celik, H. Ozkaramanli, and H. Demirel, “Facial Feature Extraction using Complex Dual-Tree Wavelet Transform,” Computer Vision and Image Understanding, Vol. 111, pp. 229-246, 2008
    11. Y. Wang, Z. He, and Y. Zi, “Enhancement of Signal Denoising and Multiple Fault Signatures Detecting in Rotating Machinery using Dual-Tree Complex Wavelet Transform,” Mechanical Systems and Signal Processing, Vol. 24, pp. 119-137, 2010
    12. J. B. Tenenbaum, V. Silva, and J. C. Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction,” Science, Vol. 290, pp. 2319-2323, 2000
    13. T. Benkedjouh, K. Medjaher, and N. Zerhouni, “Remaining Useful Life Estimation based on Nonlinear Feature Reduction and Support Vector Regression,” Engineering Applications of Artificial Intelligence, Vol. 26, pp. 1751-1760, 2013

     

     

    Please note : You will need Adobe Acrobat viewer to view the full articles.Get Free Adobe Reader

    1.        M. Unal, Y. Sahin, M. Onat, M. Denetgul, and H. Kucuk, “Fault Diagnosis of Rolling Bearings using Data Mining Techniques and Boosting,” Journal of Dynamic Systems, Measurement, and Control, Vol. 139, pp. 1-10, February 2017

    2.        A. Purarjomandlangrudi, A. H. Ghapanchi, and M. Esmalifalak “A Data Mining Approach for Fault Diagnosis: An Application of Anomaly Detection Algorithm,” Measurement, Vol. 55, pp. 343-352, 2014

    3.        E. Downham, “Vibration Monitoring and Wear Prediction,” in Proceeding of 2nd International Conference on Vibration in Rotary Machinery, pp. 29-33, 1980

    4.        Y. Li, S. Billington, and C. Zhang, “Adaptive Prognostics for Rolling Element Bearing Condition,” Mechanical Systems and Signal Processing, Vol. 13, pp. 103-113, 1999

    5.        Y. Li, T. R. Kurfess, and Y. Liang, “Stochastics Prognostics for Rolling Element Bearings,” Mechanical Systems and Signal Processing, Vol. 14, pp. 747-762, 2000

    6.        E. Momeni, R. Nazir, and D. J. Armaghani, “Prediction of Pile Bearing Capacity using a Hybrid Genetic Algorithm based ANN,” Measurement, Vol. 57, pp. 122-131, 2014

    7.        R. Huang, L. Xi, and X. Li, “Residual Life Prediction for Ball Bearings based on Self-Organizing Map and Back Propagation Neural Network Methods,” Mechanical Systems and Signal Processing, Vol. 21, pp. 193-207, 2007

    8.        N. G. Kingsbury, “The Dual-Tree Complex Wavelet Transform: A New Technique for Shift Invariance and Directional Filters,” Digital Signal Processing Workshop, Vol. 98, pp. 2-5, 1998

    9.        I. W. Selesnick, R. G. Baraniuk, and N. G. Kingsbury, “The Dual-Tree Complex Wavelet Transform,” IEEE Signal Processing Magazine, Vol. 22, pp. 123-151, 2005

    10.     T. Celik, H. Ozkaramanli, and H. Demirel, “Facial Feature Extraction using Complex Dual-Tree Wavelet Transform,” Computer Vision and Image Understanding, Vol. 111, pp. 229-246, 2008

    11.     Y. Wang, Z. He, and Y. Zi, “Enhancement of Signal Denoising and Multiple Fault Signatures Detecting in Rotating Machinery using Dual-Tree Complex Wavelet Transform,” Mechanical Systems and Signal Processing, Vol. 24, pp. 119-137, 2010

    12.     J. B. Tenenbaum, V. Silva, and J. C. Langford, “A Global Geometric Framework for Nonlinear Dimensionality Reduction,” Science, Vol. 290, pp. 2319-2323, 2000

    13.     T. Benkedjouh, K. Medjaher, and N. Zerhouni, “Remaining Useful Life Estimation based on Nonlinear Feature Reduction and Support Vector Regression,” Engineering Applications of Artificial Intelligence, Vol. 26, pp. 1751-1760, 2013

     
    This site uses encryption for transmitting your passwords. ratmilwebsolutions.com