Combination of principal component analysis and time-frequency representations of multichannel vibration data for gearbox fault detection

Radoslaw Zimroz,Pawel Stefaniak,Jacek Wodecki,Agnieszka Wylomanska,Jakub Obuchowski

doi:10.21595/jve.2016.17114

Abstract

A multichannel vibration data processing method in the context of local damage detection in gearboxes is presented in this paper. The purpose of the approach is to achieve more reliable information about local damage by using several channels in comparison to results obtained by single channel vibration analysis. The method is a combination of time-frequency representation and Principal Component Analysis (PCA) applied not to the raw time series but to each slice (along the time) from its spectrogram. Finally, we create a new time-frequency map which aggregated clearly indicates presence of the damage. Details and properties of this procedure are described in this paper, along with comparison to single-channel results. We refer to autocorrelation function of the new aggregated time frequency map (1D signal) or simple spectrum (that might be somehow linked to classical envelope analysis). The results are very convincing – cyclic impulses associated with local damage might be clearly detected. In order to validate our method, we used a model of vibration data from heavy duty gearbox exploited in mining industry.

Highlights

A problem of fault diagnosis in rotating machines has attracted attention of researchers for many years
In this paper we incorporate principal component analysis (PCA) for local damage detection in a two-stage gearbox operating in a belt conveyor driving station
In this paper we have introduced a new method of local damage detection applied to the real vibration signal from heavy duty gearbox used in mining industry

Summary

Introduction

A problem of fault diagnosis in rotating machines has attracted attention of researchers for many years. Vibration signal from a machinery system is often a mixture of several source signals. A signal acquired on a bearing operating in a belt conveyor driving station, might be contaminated with vibrations of a gearbox located nearby or by vibrations caused by other damage [11,12,13,14,15,16]. Signal acquired on a gearbox revealing multiple damage is another example of a multi-source signal [15, 16]. In this paper we incorporate principal component analysis (PCA) for local damage detection in a two-stage gearbox operating in a belt conveyor driving station. The method proved that integration of vibration signals from several channels provide a much clearer damage indication than a single signal does

Methodology

Machine and experiment description

Diagnostic data – raw multichannel signal

Evaluation of the algorithm performance on industrial data

Comparison with hypothetical individual channel processing

Conclusions