Paper Titles
Design of the Cooling Module of LEDs Based on the Aluminum-Acetone Flat Plate Heat Pipe
p.244
Development of Barrel Heating via Induction in Injection Molding Machine
p.249
Dynamics Analysis of Energy Absorbing Device Based on Damped Dynamics Vibration Absorber
p.254
Effective Numerical Analysis Method for Roll-to-Roll System Having the Large Nonlinearity Property
p.259
Enhanced Fault Diagnosis of Bearings in Gearbox Based on Lucy-Richardson Deconvolution and Discussion
p.264
Estimation for Temperature Distribution of PET Preform Surface under Multiple Lamps
p.269
Feature Extraction Technology for Rolling Bearings Based on Local Tangent Space Alignment
p.274
FEM Simulation and Experimental Verification on Rotating Compression Forming of Double-Layer Clad Ring Considering Shear Friction
p.280
Finite Element Analysis on Natural Properties of a Micro Drill with Ultrasonic Vibration
p.285

Enhanced Fault Diagnosis of Bearings in Gearbox Based on Lucy-Richardson Deconvolution and Discussion

Article Preview

Abstract:

Bearings are one of the most important components in rotating machineries. Their failures will lead to great production loss and increase the maintenance cost. So, condition monitoring work of bearings can save and avoid the potential loss caused by bearing fault. Lucy-Richardson deconvolution (LRD) algorithm, as an image processing technique, started to be used in bearing fault diagnosis. However, only data of bearings working in electric motor are used to validate the method. In engineering cases, most bearings are working in gearbox. Therefore, the bearing fault signals are very weak compared to the gear vibration signal. It is usually difficult to detect the bearing fault in this case. LRD algorithm is used to enhance the bearing fault diagnosis and some characteristics in this case are discussed. Experiment data analysis demonstrates that LRD can enhance the periodic impulse signal effectively. Otherwise, if the desired fault signal is weak compared to non-desired signal, then, the desired fault signal will be continued weaken by LRD which is not benefit to bearing’s incipient fault detection.

You might also be interested in these eBooks
Modern Design Technologies and Experiment for Advanced Manufacture and Industry View Preview

Info:

Periodical:

Applied Mechanics and Materials (Volumes 764-765)

Pages:

264-268

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.764-765.264

Citation:

Cite this paper

Online since:

May 2015

Authors:

Xing Hui Zhang*, Jian She Kang, Jian Min Zhao, Hong Zhi Teng

Keywords:

Bearing, Fault Diagnosis, Gearbox, Lucy-Richardson Deconvolution, Signal Processing

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

© 2015 Trans Tech Publications Ltd. All Rights Reserved

Share:

Citation:

* - Corresponding Author

References

[1] H.Q. Gu, J.M. Zhao and X.H. Zhang: Hybrid methodology of degradation feature extraction for bearing prognostics. Eksplotacja I Niezawodnosc-Maintenance and Reliability, Vol. 15, Issue. 2, (2013), pp.195-201.

Google Scholar

[2] H. Qiu, L. Jay, J. Lin and G. Yu: Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics. Journal of Sound and Vibration, Vol. 289, (2006), pp.1066-1090.

DOI: 10.1016/j.jsv.2005.03.007

Google Scholar

[3] K.S. Jiang, G.H. Xu, L. Liang, G.Q. Zhao and T.F. Tao: A quantitative diagnosis method for rolling element bearing using signal complexity and morphology filtering. Journal of Vibroengineering, Vol. 14, Issue. 4, (2012), pp.1862-1875.

Google Scholar

[4] X.H. Zhang, J.S. Kang and T. Jin: Degradation modeling and maintenance decision using Bayesian belief networks. IEEE Transactions on Reliability, Vol. 63, Issue. 2, (2014), pp.620-633.

DOI: 10.1109/tr.2014.2315956

Google Scholar

[5] Q. Miao, C. Tang, W. Liang and M. Pecht: Health assessment of cooling fan bearings using wavelet-based filtering. Sensors, Vol. 13, Issue. 1, (2013), pp.274-291.

DOI: 10.3390/s130100274

Google Scholar

[6] X.H. Zhang, J.S. Kang, J.S. Zhao and D.C. Cao: Features for fault diagnosis and prognosis of gearbox. Chemical Engineering Transactions, Vol. 33, (2013), pp.1027-1032.

Google Scholar

[7] X.H. Zhang, J.S. Kang, E. Bechhoefer and J.M. Zhao: A new feature extraction method for gear fault diagnosis and prognosis. Eksplotacja I Niezawodnosc-Maintenance and Reliability, Vol. 16, Issue. 2, (2014), pp.295-300.

Google Scholar

[8] N. Sawalhi, R. Randall and H. Endo: The enhancement of fault detection and diagnosis in rolling element bearings using minimum entropy deconvolution combined with spectral kurtosis. Mechanical Systems and Signal Processing, Vol. 21, (2007).

DOI: 10.1016/j.ymssp.2006.12.002

Google Scholar

[9] G.L. McDonald, Q. Zhao and M. Zuo: Maximum correlated Kurtosis deconvolution and application on gear tooth chip fault detection. Mechanical Systems and Signal Processing, Vol. 33, (2012), pp.237-255.

DOI: 10.1016/j.ymssp.2012.06.010

Google Scholar

[10] R. Santhana, and N. Murali: A novel application of Lucy-Richardson deconvolution: bearing fault diagnosis. Journal of Vibration and Control, online published, (2013).

Google Scholar

[11] W.H. Richardson: Bayesian-based iterative method of image restoration. Journal of Optical Society of America, Vol. 62, (1972), pp.55-59.

Google Scholar

[12] L.B. Lucy: An iterative technique for the rectification of observed distribution. Astronomical Journal, Vol. 79, (1974), pp.745-775.

DOI: 10.1086/111605

Google Scholar

[13] D. Wang, P.W. Tse and K.L. Tsui: An enhanced kurtogram method for fault diagnosis of rolling element bearings. Mechanical Systems and Signal Processing, Vol. 35, (2013), pp.176-179.

DOI: 10.1016/j.ymssp.2012.10.003

Google Scholar