A new statistical model for acoustic emission signals generated from sliding contact in machine elements

The proposed signal model abandons these assumptions of mild Gaussianity and/or stationarity, and it is used to derive novel indices as alternatives to the traditional RMS and kurtosis. These indices are applied to a comprehensive AE data set obtained from a pin-on-disc tribometer running over a range of sliding speeds and with specimens of different levels of surface roughness. The correlation of the indices with surface roughness is then illustrated and benchmarked against the traditional indicators. The results show that the proposed AE signal model delivers indices with a stronger correlation with surface roughness, suggesting a better representation of the tribological features associated with sliding contact.