Condition Monitoring of Drive Trains by Data Fusion of Acoustic Emission and Vibration Sensors
Oliver Mey, André Felipe Schneider, Olaf Enge‐Rosenblatt, Dirk Mayer, Christian Schmidt, Samuel Klein, Hans‐Georg Herrmann
Abstract
Early damage detection and classification by condition monitoring systems is crucial to enable predictive maintenance of manufacturing systems and industrial facilities. Data analysis can be improved by applying machine learning algorithms and fusion of data from heterogenous sensors. This paper presents an approach for a step-wise integration of classifications gained from vibration and acoustic emission sensors in order to combine the information from signals acquired in the low and high frequency ranges. A test rig comprising a drive train and bearings with small artificial damages is used for acquisition of experimental data. The results indicate that an improvement of damage classification can be obtained using the proposed algorithm of combining classifiers for vibrations and acoustic emissions.