Intelligent Gear Fault Recognition Method Based on HWP Sample Entropy and Grey Incidence

Wen Bin Zhang; Yan Ping Su; Yan Jie Zhou; Jie Min

doi:10.4028/www.scientific.net/AMR.706-708.1397

Paper Titles

Geometrical Feature Analysis of the Movement of the New Type of Double-Hemisphere Rolling Gear Globoidal Cam Indexing Mechanism
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Hysteresis Characteristic Model of Suspension for Vehicle Dynamics Simulation
p.1385

Impact of Contact Wear on Bounce Time of ZNII Vacuum Capacitor Switch
p.1389

Influence on Inertia the Moment of Inertia Matrix of Three Degrees of Freedom Air-Bearing Testbed
p.1393

Intelligent Gear Fault Recognition Method Based on HWP Sample Entropy and Grey Incidence
p.1397

Introducing Digitization into the Continuous Cooling Transformation (CCT) Curves
p.1401

Investigation of Strength and Dynamic Characteristics for the Rolling Mill
p.1405

Kinematic Analysis and Structure Parameter Optimization of Automobile Wishbone Independent Suspension
p.1409

Kinematics Simulation and Experimental Study of CVT with Diclinic Cam Plate Synchronous Revolution and Internal Power Shunt
p.1413

HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 706-708Intelligent Gear Fault Recognition Method Based on...

Intelligent Gear Fault Recognition Method Based on HWP Sample Entropy and Grey Incidence

Abstract:

In this paper, a novel method to recognize gear fault pattern was approached based on harmonic wavelet package (HWP), sample entropy and grey incidence. At first, the line structure element was selected for rank-order morphological filter to denoise the original signal. Secondly, different gear fault signals were decomposed into eight frequency bands by harmonic wavelet package in three levels; and sample entropy of each band was calculated. Finally, these sample entropies could serve as the feature vectors, the grey incidence of different gear vibration signals was calculated to identify the fault pattern and condition. Practical results show that this method can be used in gear fault diagnosis effectively.

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Periodical:

Advanced Materials Research (Volumes 706-708)

Pages:

1397-1400

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.706-708.1397

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Online since:

June 2013

Authors:

Wen Bin Zhang, Yan Ping Su, Yan Jie Zhou, Jie Min

Keywords:

Fault Recognition, Gear, Grey Incidence, Harmonic Wavelet Package, Rank-Order Morphological Filter, Sample Entropy

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References

[1] W. B. Zhang, H. J. Wang, R. J. Teng and S. K. Xu. Application of rank-order morphological filter in vibration signal de-noising, edited by Proceedings of the 2010 3rd International Congress on Image and Signal Processing, pp.4025-4027. (2010)

DOI: 10.1109/cisp.2010.5648096

Google Scholar

[2] W. B. Zhang, Y. P. Su, Y. J. Zhou, R. J. Teng and S. K. Xu. Application of rank-order morphological filter in refinement of rotor center's orbit, edited by Proceedings of the 2011 4th International Congress on Image and Signal Processing, pp.2250-2252. (2011)

DOI: 10.1109/cisp.2011.6100539

Google Scholar

[3] Y. B. Dong and X. L. Zhang. Determination method for identification coefficient of grey relational grade and applying in mechanical faults diagnosis, edited by Equipment Manufacturing Technology, Vol. 3(2008), pp.121-122, 125.

Google Scholar

[4] R. Alcaraz and J. J. Rieta. Review on sample entropy applications for the non-invasive analysis of atria fibrillation electrocardiograms, edited by Biomedical Signal Processing and Control, Vol. 5(2010), pp.1-14.

DOI: 10.1016/j.bspc.2009.11.001

Google Scholar

[5] W. B. Zhang, X. J. Zhou and Y. Lin. Harmonic wavelet package method used to extract fault signal of a rotation machine, edited by Journal of Vibration and Shock, Vol. 28(2009), pp.87-89.

Google Scholar