Recognizing Bearings’ Degradation Stage Using Multimodal Autoencoder to Learn Features from Different Time Series

Utilizing machine learning technologies to monitor assets’ health conditions can improve the effectiveness of maintenance activities. However, accurately recognizing the current health degradation stages of industrial assets requires a time-consuming manual feature extraction due to the wide range o...

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Veröffentlicht in:	SN computer science Jg. 5; H. 4; S. 371
Hauptverfasser:	Alfeo, Antonio Luca, Cimino, Mario G. C. A., Gagliardi, Guido
Format:	Journal Article
Sprache:	Englisch
Veröffentlicht:	Singapore Springer Nature Singapore 01.04.2024 Springer Nature B.V
Schlagworte:	Algorithms Classification Clustering Computer Imaging Computer Science Computer Systems Organization and Communication Networks Data integration Data Structures and Information Theory Deep learning Degradation Design Discriminant analysis Effectiveness Feature extraction Information Systems and Communication Service Innovative Intelligent Industrial Production and Logistics 2022 Machine learning Neural networks Original Research Pattern Recognition and Graphics Preventive maintenance Software Engineering/Programming and Operating Systems State of the art Time series Vibration Vision Deep feature learning Data fusion Multimodal autoencoder Contrastive learning Predictive maintenance Smart manufacturing
ISSN:	2661-8907, 2662-995X, 2661-8907
Online-Zugang:	Volltext
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