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Talks and Poster Presentations (without Proceedings-Entry):

R. Hasani, G. Wang, R. Grosu:
"A Machine Learning Suite for Machine Components´ Health-Monitoring";
Talk: AAAI 2019 - 33rd Conference on Artificial Intelligence, Honolulu, Hawaii, USA; 2019-01-29 - 2019-01-31.



English abstract:
This paper studies an intelligent technique for the healthmonitoring and prognostics of common rotary machine components, with regards to bearings in particular. During a run-to-failure experiment, rich unsupervised features from vibration sensory data are extracted by a trained sparse autoencoder. Then, the correlation of the initial samples (presumably healthy), along with the successive samples, are calculated and passed through a moving-average filter. The normalized output which is referred to as the auto-encoder correlation based (AEC) rate, determines an informative attribute of the system, depicting its health status. AEC automatically identifies the degradation starting point in the machine component. We show that AEC rate well-generalizes in several run-tofailure tests. We demonstrate the superiority of the AEC over many other state-of-the-art approaches for the health monitoring of machine bearings.

Created from the Publication Database of the Vienna University of Technology.