Abstract
The application of Acoustic Emission (AE) in condition monitoring of rotating machines has been well documented. The majority of research works in this field has involved the use of conventional time domain analysis for processing the AE signals from the machines and there has been little attention given to application of more advanced signal processing methods. This research presents a study in which some advanced signal processing techniques such as Wavelet and Spectral Kurtosis (SK) has been applied to offer improved diagnosis for bearing defect detection
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