A novel technique adapting the time-frequency analysis has been utilized to characterize stationary and non-stationary signals from tribological interactions. This representation displays time, frequency, and signal magnitude to decipher signals emanating from such interactions. Short-time Fourier transform, Wigner, Coi-Williams, and Zhao-Atlas-Marks distributions are suited to represent stationary and non-stationary signals. Some of the most complex tribological phenomena involve head-disk interactions in magnetic recording systems. Examples drawn from practical head-disk interface tests are analyzed by using the fast Fourier transform algorithm to illustrate the dynamic features of various distributions. Time-frequency representation of output spectrums of laser doppler vibrometer (LDV), strain gage sensor, and acoustic emission (AE) sensor obtained from head-disk experiments giving evidence of stationary and non-stationary behavior are investigated. (C) 1999 Published by Elsevier Science Ltd. All rights reserved.
