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Automatic classification of rotating machinery defects using Machine Learning (ML) algorithms

  • Electric machines and motors have been the subject of enormous development. New concepts in design and control allow expanding their applications in different fields. The vast amount of data have been collected almost in any domain of interest. They can be static; that is to say, they represent real-world processes at a fixed point of time. Vibration analysis and vibration monitoring, including how to detect and monitor anomalies in vibration data are widely used techniques for predictive maintenance in high-speed rotating machines. However, accurately identifying the presence of a bearing fault can be challenging in practice, especially when the failure is still at its incipient stage, and the signal-to-noise ratio of the monitored signal is small. The main objective of this work is to design a system that will analyze the vibration signals of a rotating machine, based on recorded data from sensors, in the time/frequency domain. As a consequence of such substantial interest, there has been a dramatic increase of interest in applying Machine Learning (ML) algorithms to this task. An ML system will be used to classify and detect abnormal behavior and recognize the different levels of machine operation modes. The proposed solution can be deployed as predictive maintenance for Industry 4.0.

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Author of HS ReutlingenZimmermann, Alfred
Erschienen in:Human centred intelligent systems : proceedings of KES-HCIS 2020 Conference. - (Smart innovation, systems and technology ; 189)
Place of publication:Singapore
Editor:Alfred ZimmermannORCiD
Document Type:Conference proceeding
Publication year:2021
Page Number:11
First Page:193
Last Page:203
PPN:Im Katalog der Hochschule Reutlingen ansehen
DDC classes:650 Management
Open access?:Nein