Local Geometric Projection-Based Noise Reduction for Vibration Signal Analysis in Rolling Bearings

Published

October 31, 2008

Author(s)

Kang B. Lee, Steven E. Fick, Robert Gao, Ruqiang Yan

Abstract

This paper presents a noise reduction technique for vibration signal analysis in rolling bearings, based on local geometric projection (LGP). LGP is a non-linear filtering technique that reconstructs one dimensional time series in a high-dimensional phase space using time-delayed coordinates, based on the Takens embedding theorem. From the neighborhood of each point in the phase space, where a neighbor is defined as a local subspace of the whole phase space, the best subspace to which the point will be orthogonally projected is identified. Since the signal subspace is formed by the most significant eigen-directions of the neighborhood, while the less significant ones define the noise subspace, the noise can be reduced by converting the points onto the subspace spanned by those significant eigen-directions back to a new, one-dimensional time series. Improvement on signal-to-noise ratio enabled by LGP is first evaluated using a chaotic system and an analytically formulated synthetic signal. Then, analysis of bearing vibration signals is carried out as a case study. The LGP based technique is shown to be effective in reducing noise and enhancing extraction of weak, defect-related features, as manifested by the multifractal spectrum from the signal.

Proceedings Title

Proceedings of IMECE2008

Conference Dates

October 31-November 6, 2008

Conference Location

Boston, MA

Conference Title

2008 ASME International Mechanical Engineering Congress and Exposition

Pub Type

Conferences

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Keywords

Health monitoring, local geometric projection, noise reduction, phase space reconstruction.

Citation

Lee, K. , Fick, S. , Gao, R. and Yan, R. (2008), Local Geometric Projection-Based Noise Reduction for Vibration Signal Analysis in Rolling Bearings, Proceedings of IMECE2008, Boston, MA, [online], https://tsapps.nist.gov/publication/get_pdf.cfm?pub_id=824731 (Accessed December 17, 2024)