Defect Detection for Bearings Using Envelope Spectra of Wavelet Transform

Abstract

In order to overcome the shortcomings in the traditional envelope analysis in which manually specifying a resonant frequency band is required, a new approach based on the fusion of the wavelet transform and envelope spectrum is proposed for detecting and localizing defects in rolling element bearings. This approach is capable of completely extracting the characteristic frequencies related to the defect from the resonant frequency band. Based on the Shannon entropy of wavelet-based envelope spectra, a criterion to select optimal scale to monitor the condition of bearings is also presented. Experiment results show that the proposed approach is sensitive and reliable in detecting defects on the outer race, inner race, and rollers of bearings.

Time-frequency representation based on time-varying autoregressive model with applications to non-stationary rotor vibration analysis

“…Meanwhile, the division of frequency band by the DWT is too coarse to offer an exact characterization of mechanical vibration signals. As such, the CWT has gaining a lot of applications in vibration signal analysis for fault diagnosis (Chiementin et al 2007;Gryllias & Antoniadis 2009;Rubini & Meneghetti 2001;Shi et al 2004;Nagaraju et al 2009) due to the fact that CWT is able to decompose the signals into a fine divided frequency grid. However, this advantage is based on the expense of computational efficiency.…”

Optimal Wavelet Selection for the Size Estimation of Manufacturing Defects of Tapered Roller Bearings with Vibration Measurement using Shannon Entropy Criteria

“…combination of envelope spectrum and wavelet transform for the extraction of defect problems in bearings were used by Shi et al [8].…”

Using support vector machines and acoustic noise signal for degradation analysis of rotating machinery

“…Detecting and diagnosing defects in REBs by manual visual inspection of the vibration or acoustic measurements has been reported in the literature (Tandon and Nakra 1990;Heng and Nor 1998;Shi et al 2004). However, such manual approaches are inefficient, require expert training and are subjective.…”

“…These can be broadly classified into time domain techniques such as root-mean-square, crest factor, and statistical parameters such as mean, variance, skewness and kurtosis (Tandon and Choudhury 1999;Heng and Nor 1998) and frequency domain techniques such as spectral, Short Time Fourier Transform (STFT), Wavelet Transform (WT) and envelope detection (McFadden and Smith 1984;Shi et al 2004;Tandon and Choudhury 1999).…”