Acoustic signals (AE) have become a valuable tool for monitoring various tribological systems. This study in-
vestigates three key tribological behaviors: the initial running-in process, severe wear, and the lubrication state
of journal bearings using four AE postprocessing methods. These methods are friction power-scaled quadratic
mean (SRMS), power spectrum (PSD), normalized power spectrum (nPSD), and a statistical hit-rate (HR) analysis
of the raw AE signal. The research draws on data from four tests conducted in three set-ups: Ring-on-Disc (RoD),
Ring-on-Liner (RoL), and Journal-bearing-adapter (JBA) test configurations. AE measurements were taken at
both 1 Hz and 1 kHz for the quadratic mean value (RMS), as well as at 900 kHz for short time intervals during
each test to capture the raw signal data. In conjunction with other measured parameters like friction coefficient,
temperature, contact potential, and wear, we establish correlations between the acoustic parameters and
tribological behaviors. The findings indicate that SRMS and nPSD are valuable for identifying the end of the
initial running-in phase. All four methods are effective in recognizing high wear states, with nPSD and HR even
distinguishing between different wear phenomena. The lubrication state of journal bearings is best assessed using
nPSD, allowing users to monitor certain geometric changes in the bearing.