LAUSR

Abstract This paper proposes a new statistical model to characterise acoustic emission (AE) signals generated from surfaces in sliding contact. Such signals have traditionally been assessed using simple metrics such as RMS or kurtosis, but these metrics assume the signal is both (approximately) Gaussian and stationary, neither of which hold in many practical cases. Sliding contact generally involves the breaking or plastic deformation of surface asperities, producing an impulsive, highly non-Gaussian AE signal. If the sliding contact occurs in a rotating or reciprocating machine, such as from gears, the generated signals are usually not stationary, but rather cyclostationary, with periodic fluctuations in signal power tied to the rotation of the machine. 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.