On the use of machine learning methods to predict component reliability from data-driven industrial case studies-Reference-Cited by-同舟云学术

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On the use of machine learning methods to predict component reliability from data-driven industrial case studies

Published:2017-09-11 Issue:5-8 Volume:94 Page:2419-2433
ISSN:0268-3768
Container-title:The International Journal of Advanced Manufacturing Technology
language:en
Short-container-title:Int J Adv Manuf Technol

Author:

Alsina Emanuel F.,Chica Manuel,Trawiński Krzysztof,Regattieri Alberto

Funder

Ministerio de Economía y Competitividad

Publisher

Springer Science and Business Media LLC

Subject

Industrial and Manufacturing Engineering,Computer Science Applications,Mechanical Engineering,Software,Control and Systems Engineering

Link

http://link.springer.com/article/10.1007/s00170-017-1039-x/fulltext.html

Reference65 articles.

1. Birolini A (2007) Reliability engineering, vol 5. Springer, Berlin

2. Shi H, Zeng J (2016) Real-time prediction of remaining useful life and preventive opportunistic maintenance strategy for multi-component systems considering stochastic dependence. Comput Ind Eng 93:192–204

3. Luo M, Yan HC, Hu B, Zhou JH, Pang CK (2015) A data-driven two-stage maintenance framework for degradation prediction in semiconductor manufacturing industries. Comput Ind Eng 85:414–422

4. Sun Y, Ma L, Mathew J (2009) Failure analysis of engineering systems with preventive maintenance and failure interactions. Comput Ind Eng 57(2):539–549

5. Rodger J A, George JA (2017) Triple bottom line accounting for optimizing natural gas sustainability: a statistical linear programming fuzzy ILOWA optimized sustainment model approach to reducing supply chain global cybersecurity vulnerability through information and communications technology. J Clean Prod 142:1931–1949

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