Prognosis of Bearing Acoustic Emission Signals Using Supervised Machine Learning

Acoustic emission (AE) technique can be successfully utilized for condition monitoring of various machining and industrial processes. To keep machines function at optimal levels, fault prognosis model to predict the remaining useful life (RUL) of machine components is required. This model is used to...

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Bibliographic Details
Published in:	IEEE transactions on industrial electronics (1982) 2018-07, Vol.65 (7), p.5864-5871
Main Authors:	Elforjani, Mohamed, Shanbr, Suliman
Format:	Article
Language:	English
Subjects:	Acoustic emission Acoustic emission (AE) Artificial intelligence artificial neural network (ANN) Artificial neural networks Back propagation algorithms Back propagation networks Bearings condition monitoring Data models Downtime Feature extraction Gaussian process gaussian process regression (GPR) Machine learning Machinery condition monitoring Machining Management systems Multilayers Neural networks Parameter estimation Predictive models Prognosis Regression analysis Regression models remaining useful life (RUL) slow speed bearings support vector machine regression (SVMR) Support vector machines Vibrations
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