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Condition monitoring of drive trains by data fusion of acoustic emission and vibration sensors

 
: Mey, Oliver; Schneider, André; Enge-Rosenblatt, Olaf; Mayer, Dirk; Schmidt, Christian; Klein, Samuel; Herrmann, Hans-Georg

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Volltext (PDF; - Gesamter Tagungsband)

Yurish, S.Y. ; International Frequency Sensor Association -IFSA-, Brussels:
1st IFSA Winter Conference on Automation, Robotics & Communications for Industry 4.0, ARCI 2021. Proceedings : 3-5 February 2021, Chamonix-Mont-Blanc, France
Barcelona: IFSA Publishing, 2021
ISBN: 978-84-09-27538-0
S.52-56
Winter Conference on Automation, Robotics & Communications for Industry 4.0 (ARCI) <1, 2021, Chamonix-Mont-Blanc>
Englisch
Konferenzbeitrag, Elektronische Publikation
Fraunhofer IIS, Institutsteil Entwurfsautomatisierung (EAS) ()
Fraunhofer IZFP ()
condition monitoring; vibration; acoustic emission; drive train; data fusion; machine learning

Abstract
Early damage detection and classification by condition monitoring systems is crucial to enable predictive maintenance of manufacturing systems and industrial facilities. The 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 range. 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 emission.

: http://publica.fraunhofer.de/dokumente/N-635230.html