TY  - CHAP
U1  - Konferenzveröffentlichung
A1  - Zoungrana, Wend-Benedo
A1  - Chehri, Abdellah
A1  - Zimmermann, Alfred
ED  - Zimmermann, Alfred
T1  - Automatic classification of rotating machinery defects using Machine Learning (ML) algorithms
T2  - Human centred intelligent systems : proceedings of KES-HCIS 2020 Conference. - (Smart innovation, systems and technology ; 189)
N2  - 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.
Y1  - 2021
SN  - 978-981-15-5783-5
SB  - 978-981-15-5783-5
U6  - https://doi.org/10.1007/978-981-15-5784-2_16
DO  - https://doi.org/10.1007/978-981-15-5784-2_16
SP  - 193
EP  - 203
S1  - 11
PB  - Springer
CY  - Singapore
ER  -