Effect of Parameters Setting on Performance of Discrete Component Removal (DCR) Methods for Bearing Faults Detection

Bovic Kilundu, Agusmian Partogi, Faris Elasha, and David Mba
Submission Type: 
Full Paper
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phmce_14_062.pdf1.38 MBMay 25, 2014 - 11:54pm

Detecting bearing faults on rotating machinery based on vibration signals is often a challenge due to the high energy (dominant) signals originating from gears, screws, and/or shafts that can mask weak signal (i.e. non-deterministic) generated by bearing faults. These dominant signals are deterministic, meaning that they will appear as discrete components in the frequency domain. When bearing faults detection is of interest, it is therefore important to remove these discrete components prior to applying further signal processing. Several methods have been proposed in literature for separating discrete components and non-deterministic components (i.e. residual signals) useful for bearing fault detection. The choice of setting parameters when applying these methods can have a significant effect on the residual signals. This paper compares the performance of bearing fault detection after applying different DCR methods. Here, three methods are evaluated, namely synchronous average, synchronous adaptive noise cancellation and cepstrum editing. A qualitative comparison of different methods has also been recently performed by Randall et al. However, to the authors’ knowledge, the effects of different parameters setting on the performance of bearing fault detection have not discussed yet elsewhere. To fill this gap, this paper aims at discussing the effects of parameters setting and eventually providing a quantitative comparison. In cepstrum editing, the width of the notch “lifter” is an important parameter. When using TSA to separate non- and deterministic components, the case of a multiple shafts situation, the number of averages as well as the quality of the tachometer signal have to be considered. For demonstration purposes, these three methods have been applied for bearing faults detection on vibration signals measured on two gearboxes, namely (i) a laboratory gearbox used in PHM09 data competition and (ii) an industrial gearbox which is a part of a transmission driveline on the actuation mechanism of secondary control surface in civil aircraft. The residual signals from these 3 methods are processed following the optimized envelope analysis by using spectral kurtosis for determining the optimal frequency band for demodulation.. Synchronous adaptive noise cancellation gives acceptable results. Cepstrum editing results in the best separation.

Publication Year: 
2014
Publication Volume: 
5
Publication Control Number: 
062
Page Count: 
8
Submission Keywords: 
Bearing Faults
vibration
Discrete Component Removal (DCR)
Submission Topic Areas: 
Component-level PHM
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