Hybrid data augmentation method for combined failure recognition in rotating machines

Rotating machines are frequently subject to a wide range of rough conditions, resulting in mechanical failures and performance degradation. Thus, it is important to apply proper failure detection and recognition techniques, such as machine learning algorithms, to prevent these issues early. In indus...

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Bibliographic Details
Published in:	Journal of intelligent manufacturing Vol. 34; no. 4; pp. 1795 - 1813
Main Authors:	Martins, Dionísio H. C. S. S., de Lima, Amaro A., Pinto, Milena F., Hemerly, Douglas de O., Prego, Thiago de M., e Silva, Fabrício L., Tarrataca, Luís, Monteiro, Ulisses A., Gutiérrez, Ricardo H. R., Haddad, Diego B.
Format:	Journal Article
Language:	English
Published:	New York Springer US 01.04.2023 Springer Nature B.V
Subjects:	Advanced manufacturing technologies Algorithms Business and Management Classification Control Data augmentation Failure Failure detection Machine learning Machines Manufacturing Mechatronics Performance degradation Processes Production Random noise Recognition Robotics Rotating machinery Rotating machines Support vector machines Combined failures recognition Data augmentation Imbalance Rotating machines Misalignment Predictive maintenance
ISSN:	0956-5515, 1572-8145
Online Access:	Get full text
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