Gear fault diagnosis using bispectrum analysis of active noise cancellation-based filtered sound and vibration signals

Abstract

Fault diagnosis using acoustical and vibration signal processing has received strong attention from many researchers over the last two decades. In the present work, the experiment has been carried out with a customized gear mesh test setup in which the defects have been introduced in the driver gear. Classical statistical analysis including higher-order statistics, namely bispectrum analysis, has been incorporated to detect the defects. However, in order to improve the signal-to-noise ratio of the captured signals for accurate defect detection, an adaptive filtering has been proposed. Active noise cancellation (ANC) has been applied on the acoustical and vibration signals as a denoising filter. The least mean square based ANC technique has been implemented considering the signals from healthy gear meshing as the background noise. The focus of this experimental research is to evaluate the appropriateness of the ANC technique as a denoising tool and the subsequent bispectrum analysis for identifying the defects. The performance of the ANC filtering was evaluated with most widely accepted standard filters. A synthetic signal, close in nature to the actual signal, has been investigated to ascertain the adequacy.