Condition monitoring can help to detect faults of rotating machinery early and thereby prevent failures. Rolling element bearings are one of the most important machine elements to be monitored. This study focusses on rolling element bearing fault detection and localization using high-frequency, structure-borne sound, so-called acoustic emissions (AE) sensors on a dedicated roller bearing test bench. One the one hand, the high-frequency signals (range 20–1000 kHz) are analyzed and on the other hand, a demodulation algorithm is employed to down-sample the signals to frequency range of common bearing frequencies (≤10 kHz) to allow a state-of-the-art fault localization using spectral analysis of these signals. The AE results are also compared to the commonly used spectral analysis of vibration signals using conventional, piezo-electric acceleration sensors (≤10 kHz). The results show that AE is on par with vibration signals for fault localization and outperforms vibration in detecting very small surface damages and starved-lubrication conditions.
Gregarek et al. (2026) studied this question.