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Comparison of denoising schemes and dimensionality reduction techniques for fault diagnosis of rolling element bearing using wavelet transform

Comparison of denoising schemes and dimensionality reduction techniques for fault diagnosis of... This paper presents the evaluation of five wavelets-based denoising schemes in order to select the best possible scheme for denoising bearing vibration signals and dimensionality reduction techniques using artificial neural network (ANN). Vibration signals from four conditions of rolling element bearing (REB) namely normal (N), defect on inner race (IR), defect on ball (B) and defect on outer race (OR) have been denoised using interval-dependent Copyright © 2016 Inderscience Enterprises Ltd. Comparison of denoising schemes and dimensionality reduction techniques 239 denoising scheme, which is the best possible scheme. The denoised signal is subjected to discrete wavelet transform (DWT) to extract 17 statistical features. Principal component analysis (PCA)-based dimensionality reduction technique (DRT) namely PCA alone, Kernel-PCA (KPCA) alone, PCA using SVD and KPCA using SVD have been used for reducing the dimension of the features. It is found that KPCA using SVD resulted in highest prediction accuracy using ANN, making it suitable for effective REB fault diagnosis. [Received 30 November 2015; Revised 17 June 2016; Accepted 20 June 2016] Keywords: vibration signal; wavelet transform; wavelet-based denoising; Kernel-PCA; KPCA; artificial neural network; ANN; SVD. Reference to this paper should be made as follows: Kumar, H.S., Srinivasa Pai1, P., Sriram, N.S., http://www.deepdyve.com/assets/images/DeepDyve-Logo-lg.png International Journal of Manufacturing Research Inderscience Publishers

Comparison of denoising schemes and dimensionality reduction techniques for fault diagnosis of rolling element bearing using wavelet transform

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Publisher
Inderscience Publishers
Copyright
Copyright © 2016 Inderscience Enterprises Ltd.
ISSN
1750-0591
eISSN
1750-0605
DOI
10.1504/IJMR.2016.079461
Publisher site
See Article on Publisher Site

Abstract

This paper presents the evaluation of five wavelets-based denoising schemes in order to select the best possible scheme for denoising bearing vibration signals and dimensionality reduction techniques using artificial neural network (ANN). Vibration signals from four conditions of rolling element bearing (REB) namely normal (N), defect on inner race (IR), defect on ball (B) and defect on outer race (OR) have been denoised using interval-dependent Copyright © 2016 Inderscience Enterprises Ltd. Comparison of denoising schemes and dimensionality reduction techniques 239 denoising scheme, which is the best possible scheme. The denoised signal is subjected to discrete wavelet transform (DWT) to extract 17 statistical features. Principal component analysis (PCA)-based dimensionality reduction technique (DRT) namely PCA alone, Kernel-PCA (KPCA) alone, PCA using SVD and KPCA using SVD have been used for reducing the dimension of the features. It is found that KPCA using SVD resulted in highest prediction accuracy using ANN, making it suitable for effective REB fault diagnosis. [Received 30 November 2015; Revised 17 June 2016; Accepted 20 June 2016] Keywords: vibration signal; wavelet transform; wavelet-based denoising; Kernel-PCA; KPCA; artificial neural network; ANN; SVD. Reference to this paper should be made as follows: Kumar, H.S., Srinivasa Pai1, P., Sriram, N.S.,

Journal

International Journal of Manufacturing Research – Inderscience Publishers

Published: Jan 1, 2016

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