Performance of Envelope Demodulation for Bearing Damage Detection on CWRU Accelerometric Data: Kurtogram and Traditional Indicators vs. Targeted a Posteriori Band Indicators

Daga Alessandro Paolo,Marchesiello Stefano,Garibaldi Luigi,Fasana Alessandro

doi:10.3390/app11146262

Daga Alessandro Paolo, Marchesiello Stefano + Show 2 more

Open Access

https://doi.org/10.3390/app11146262

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Journal: Applied sciences	Publication Date: Jul 6, 2021
Citations: 6	License type: CC BY 4.0

Affiliation: Polytechnic University of Turin

Abstract

Envelope demodulation of vibration signals is surely one of the most successful methods of analysis for highlighting diagnostic information of rolling element bearings incipient faults. From a mathematical perspective, the selection of a proper demodulation band can be regarded as an optimization problem involving a utility function to assess the demodulation performance in a particular band and a scheme to move within the search space of all the possible frequency bands {f, Δf} (center frequency and band size) towards the optimal one. In most of cases, kurtosis-based indices are used to select the proper demodulation band. Nevertheless, to overcome the lack of robustness to non-Gaussian noise, different utility functions can be found in the literature. One of these is the kurtosis of the unbiased autocorrelation of the squared envelope of the filtered signal found in the autogram. These heuristics are usually sufficient to highlight the defect spectral lines in the demodulated signal spectrum (i.e., usually the squared envelope spectrum (SES)), enabling bearings diagnostics. Nevertheless, it is not always the case. In this work, then, posteriori band indicators based on SES defect spectral lines are proposed to assess the general envelope demodulation performance and the goodness of traditional indicators. The Case Western Reserve University bearing dataset is used as a test case.

Highlights

Rolling element bearings (REBs) are fundamental components of most power transmission systems involving rotating shafts, and as such, it is important that they are monitored so as to avoid catastrophic accidents
The selection of a proper demodulation band can be regarded as an optimization problem involving a utility function to assess the demodulation performance in a particular band and a scheme to move within the search space of all the possible frequency bands {f, ∆f} towards the optimal one
One of these is the kurtosis of the unbiased autocorrelation of the squared envelope of the filtered signal found in the autogram

Summary

Introduction

Rolling element bearings (REBs) are fundamental components of most power transmission systems involving rotating shafts, and as such, it is important that they are monitored so as to avoid catastrophic accidents. Vibration signals exhibit damage-characteristic signatures: in the time domain, series of transients (i.e., impulse responses) could be found to repeat at specific defect frequencies that depended on the defect location (i.e., the rolling element, the outer or inner rings or the cage) [2]. Milestones for the band selection task are the spectral kurtosis (SK) [4] and the kurtogram [5], which uses as indicator (i.e., utility function) the kurtosis of the coefficients at the output of quasi-analytic filters with different central frequencies CF and bandwidths BF. In [10], for example, a frequency-domain “protrusion” was introduced in the protrugram In this case the utility function corresponds to the kurtosis of the amplitudes of the envelope spectrum of the demodulated signal. The well-known Case Western Reserve University bearing dataset will be used as a test case

Methodology and Band Indicators

Findings

Brief Description of the CWRU Bearing Data Center Test Rig