Gearbox diagnostics using wavelet-based windowing technique

In extracting gear box acoustic signals embedded in excessive noise, the need for an online and automated tool becomes a crucial necessity. One of the recent approaches that have gained some acceptance within the research arena is the Wavelet multi-resolution analysis (WMRA). However selecting an accurate mother wavelet, defining dynamic threshold values and identifying the resolution levels to be considered in gearboxes fault detection and diagnosis are still challenging tasks. This paper proposes a novel wavelet-based technique for detecting, locating and estimating the severity of defects in gear tooth fracture. The proposed technique enhances the WMRA by decomposing the noisy data into different resolution levels while data sliding it into Kaiser's window. Only the maximum expansion coefficients at each resolution level are used in de-noising, detecting and measuring the severity of the defects. A small set of coefficients is used in the monitoring process without assigning threshold values or performing signal reconstruction. The proposed monitoring technique has been applied to a laboratory data corrupted with high noise level.