A systematic literature review of machine learning methods applied to predictive maintenance

Author:

Carvalho Thyago P.,Soares Fabrízzio A. A. M. N.,Vita Roberto,Francisco Roberto da P.,Basto João P.,Alcalá Symone G. S.

Funder

Brazilian Ministry of Science, Technology and Innovation

Publisher

Elsevier BV

Subject

General Engineering,General Computer Science

Reference79 articles.

1. Abbas, A. K., Al-haideri, N. A., & Bashikh, A. A. (2019). Implementing artificial neural networks and support vector machines to predict lost circulation. Egyptian Journal of Petroleum (pp. 1–9). In press.

2. Failure prediction methodology for improved proactive maintenance using bayesian approach;Abu-Samah;IFAC-PapersOnLine,2015

3. An industrial case study using vibration data and machine learning to predict asset health;Amihai,2018

4. Modeling machine health using gated recurrent units with entity embeddings and k-means clustering;Amihai,2018

5. A research study on unsupervised machine learning algorithms for fault detection in predictive maintenance;Amruthnath,2018

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