Fault Detection of a Roller-Bearing System through the EMD of a Wavelet Denoised Signal

Sensors

Dae-Ho Kwak¹  Jong-Hyo Ahn¹  Bong-Hwan Koh¹ 

[1] Department of Mechanical, Robotics and Energy Engineering, Dongguk University-Seoul,30 Pildong-ro 1-gil, Jung-gu, Seoul 100-715, Korea;

关键词: fault detection;    wavelet de-noising;    empirical mode decomposition;    intrinsic mode function;    proper orthogonal value;   

DOI  :  10.3390/s140815022

来源: DOAJ

【 摘 要 】

This paper investigates fault detection of a roller bearing system using a wavelet denoising scheme and proper orthogonal value (POV) of an intrinsic mode function (IMF) covariance matrix. The IMF of the bearing vibration signal is obtained through empirical mode decomposition (EMD). The signal screening process in the wavelet domain eliminates noise-corrupted portions that may lead to inaccurate prognosis of bearing conditions. We segmented the denoised bearing signal into several intervals, and decomposed each of them into IMFs. The first IMF of each segment is collected to become a covariance matrix for calculating the POV. We show that covariance matricesfrom healthy and damaged bearings exhibit different POV profiles, which can be adamage-sensitive feature. We also illustrate the conventional approach of feature extraction, of observing the kurtosis value of the measured signal, to compare the functionality of the proposed technique. The study demonstrates the feasibility ofwavelet-based de-noising, and shows through laboratory experiments that tracking the proper orthogonal values of the covariance matrix of the IMF can be an effective and reliable measure for monitoring bearing fault.

【 授权许可】

Unknown