Application of data-driven models to predictive maintenance: Bearing wear prediction at TATA steel-Reference-Cited by-同舟云学术

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Application of data-driven models to predictive maintenance: Bearing wear prediction at TATA steel

Published:2021-12 Issue: Volume:186 Page:115699
ISSN:0957-4174
Container-title:Expert Systems with Applications
language:en
Short-container-title:Expert Systems with Applications

Author:

Chen X.,Van Hillegersberg Jos,Topan E.,Smith S.,Roberts M.

Funder

University of Twente

Publisher

Elsevier BV

Subject

Artificial Intelligence,Computer Science Applications,General Engineering

Reference21 articles.

1. Machine tools with hidden defects: Optimal usage for maximum lifetime value;Akcay;IISE Transactions,2020

2. A research study on unsupervised ma- chine learning algorithms for early fault detection in predictive maintenance;Amruthnath,2018

3. Fault class prediction in unsupervised learning using model-based clustering approach;Amruthnath,2018

4. A random forest guided tour;Biau;Test,2016

5. Brown, M. S. (2015). What IT Needs To Know About The Data Mining Process. Published by Forbes, 29.

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4. Predictive maintenance in Industry 4.0: A systematic multi-sector mapping;CIRP Journal of Manufacturing Science and Technology;2024-06

5. HSMM multi-observations for prognostics and health management;Proceedings of the Institution of Mechanical Engineers, Part O: Journal of Risk and Reliability;2024-03-24

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