Fault Diagnosis of Gear Wear Based on Local Mean Decomposition

Hui Li

doi:10.4028/www.scientific.net/AMR.459.298

Paper Titles

The Research of Bivariate Minimum-Energy Frames and Frames of Subspace and Application in Particle Physics
p.280

Yarn Feeding Change Analysis for Needle Bar Transverse Shifting of Carpet Tufting Machine
p.284

The Nice Feature of a Sort of Orthogonal Finitely Supported Five-Variant Wavelet Packages
p.289

Wrist Wearable Physiological Signal Detection Equipment with Biochemical Materials
p.293

Fault Diagnosis of Gear Wear Based on Local Mean Decomposition
p.298

Technology to Drill Micro-Holes without Recast Layer by Laser on Nickel-Based Alloy
p.303

Research on Compensation Method of Temperature Drift in Pressure Sensor Using Double Wheatstone-Bridge Method
p.311

Study on Recast Phenomenon in Processing of Micro-Hole with Millisecond Laser
p.315

3-D Image Reconstruction in Optical Coherence Tomography Systems Based on VTK in OCT Systems
p.320

HomeAdvanced Materials ResearchAdvanced Materials Research Vol. 459Fault Diagnosis of Gear Wear Based on Local Mean...

Fault Diagnosis of Gear Wear Based on Local Mean Decomposition

Abstract:

A new approach to fault diagnosis of gear wear based on Local mean decomposition (LMD) is proposed. Local mean decomposition can adaptively decomposes the vibration signal into a series of product functions (PFs), which is the product of an envelope signal and a frequency modulated signal. LMD is capable of revealing interesting feature embedded in the signal. The experimental examples are conducted to evaluate the effectiveness of the proposed approach. The experimental results provide strong evidence that the performance of the approach based on local mean decomposition is better to extract the fault characteristics of the faulty gear and can effectively diagnose the gear wear fault.

You might also be interested in these eBooks

View Preview

Info:

Periodical:

Advanced Materials Research (Volume 459)

Pages:

298-302

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.459.298

Citation:

Cite this paper

Online since:

January 2012

Authors:

Hui Li

Keywords:

Fault Diagnosis, Gear, Local Mean Decomposition (LMD), Signal Processing, Time Frequency Analysis

Export:

RIS, BibTeX

Price:

Permissions CCC:

Request Permissions

Permissions PLS:

Request Permissions

Сopyright:

Citation:

References

[1] J. Lin and M.J. Zuo, Gearbox fault diagnosis using adaptive wavelet filter, Mechanical Systems and Signal Processing, Vol. 17, No. 6, (2003), p.1259–1269.

DOI: 10.1006/mssp.2002.1507

Google Scholar

[2] Hui Li, Yuping Zhang, Haiqi Zheng. Application of Hermitian wavelet to crack fault detection in gearbox. Mechanical Systems and Signal Processing, Vol. 25, No. 4, (2011), pp.1353-1363.

DOI: 10.1016/j.ymssp.2010.11.008

Google Scholar

[3] Hui Li, Yuping Zhang, Haiqi Zheng: Hilbert-Huang transform and marginal spectrum for detection and diagnosis of localized defects in roller bearings. Journal of Mechanical Science and Technology, Vol. 23, No. 2, (2009), pp.291-301.

DOI: 10.1007/s12206-008-1110-5

Google Scholar

[4] N. E Huang, Z. Shen and S.R. Long, et al: The empirical mode decomposition and the Hilbert spectrum for nonlinear and non-stationary time series analysis. Proceeding of Royal Society London, Series A, Vol. 454, (1998), pp.903-995.

DOI: 10.1098/rspa.1998.0193

Google Scholar

[5] J.S. Smith: The local mean decomposition and its application to ECG perception data. International Journal of the Royal Society Interface, Vol. 2, No. 5, (2005), pp.443-454.

DOI: 10.1098/rsif.2005.0058

Google Scholar