The measurement of acoustic emission data in experiments reveals informative details about the tribological contact. The required recording rate for conclusive datasets ranges up to several megahertz. Typically, this results in very large datasets for long-term measurements. This in return has the consequence, that acoustic emissions are mostly acquired at predefined cyclic time intervals, which leads to many blind spots. The following work shows methods for effective postprocessing and a feature based data acquisition method. Additionally, a two stage wear mechanism for bearings was found by the described method and could be substantiated by a numerical simulation.

Acoustic emissions from tribological contacts have become an interesting field of science in recent years. This study focuses on predicting the friction power of a given system (lubricated ring-on-disc geometry), independently of the used sliding material and lubricant, from the acoustic emissions emitted from the system. The low-frequency (1 Hz), continuously measured RMS value of the acoustic data is combined with short-duration and high-frequency (850 kHz) signal data in form of the power spectra and hit rate with three prominence levels. The classification system then predicts the friction power of the test system continuously over the whole test time. Prediction is achieved by four different machine learning methods (tree-type, support vector machine, K-nearest-neighbor, neural network) trained with data from 54 ring-on-disc tests with high variation in material and oil combinations. The method allows for the quantifiable and step-free prediction of absolute values of friction power with accuracy of 97.6% on unseen data, with a weighted K-nearest-neighbor classifier, at any point in time during an experiment. The system reacts well to rapid changes in friction conditions due to changes in load and temperature. The study shows the high information degree of acoustic emissions, concerning the actual friction mechanisms occurring and the quantitative, and not only qualitative, information that one can gain about a tribological system by analyzing them.