Remaining Useful Life Estimation of Rotating Machines through Supervised Learning with Non-Linear Approaches

Remaining Useful Life Estimation of Rotating Machines through Supervised Learning with Non-Linear Approaches

Bearings are one of the most common causes of failure for rotating electric machines. Intelligent condition-based monitoring (CbM) can be used to predict rolling element bearing fault modes using non-invasive and inexpensive sensing. Strategically placed accelerometers can acquire bearing vibration...

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Bibliographic Details
Main Authors: Eoghan T. Chelmiah, Violeta I. McLoone, Darren F. Kavanagh
Format: Article
Language:English
Published: MDPI AG 2022-04-01
Series:Applied Sciences
Subjects:
mechanical bearings
signal processing
fault detection
feature extraction
frequency domain analysis
machine learning
Online Access:https://www.mdpi.com/2076-3417/12/9/4136

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https://www.mdpi.com/2076-3417/12/9/4136

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