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A systematic literature review of machine learning methods applied to predictive maintenance
Thyago Peres Carvalho,Fabrizzio Soares,Fabrizzio Soares,Roberto Vita,Roberto da Piedade Francisco,Joao Pedro Tavares Vieira Basto,Symone Gomes Soares Alcalá +6 more
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TLDR
A systematic literature review of ML methods applied to PdM, showing which are being explored in this field and the performance of the current state-of-the-art ML techniques.About:
This article is published in Computers & Industrial Engineering.The article was published on 2019-11-01. It has received 543 citations till now. The article focuses on the topics: Predictive maintenance.read more
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Predictive maintenance in the Industry 4.0: A systematic literature review
Tiago Zonta,Cristiano André da Costa,Rodrigo da Rosa Righi,Miromar Jose de Lima,Eduardo Silveira da Trindade,Guann-Pyng Li +5 more
TL;DR: It was concluded that computer science, including artificial intelligence and distributed computing fields, is more and more present in an area where engineering was the dominant expertise, so detaching the importance of a multidisciplinary approach to address Industry 4.0 effectively.
Journal ArticleDOI
The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions
Aline Borges,Fernando José Barbin Laurindo,Mauro de Mesquita Spinola,Rodrigo Franco Gonçalves,Claudia Aparecida de Mattos +4 more
TL;DR: A critical literature review related to the integration of AI to organizational strategy is provided, synthetizing the existing approaches and frameworks, highlighting the potential benefits, challenges and opportunities and presenting a discussion about future research directions.
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Machine Learning in Predictive Maintenance towards Sustainable Smart Manufacturing in Industry 4.0
Zeki Murat Cinar,Abubakar Abdussalam Nuhu,Qasim Zeeshan,Orhan Korhan,Mohammed Asmael,Babak Safaei +5 more
TL;DR: This paper aims to provide a comprehensive review of the recent advancements of ML techniques widely applied to PdM for smart manufacturing in I4.0 by classifying the research according to the ML algorithms, ML category, machinery, and equipment used, and highlight the key contributions of the researchers, and thus offers guidelines and foundation for further research.
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Data-driven predictive maintenance planning framework for MEP components based on BIM and IoT using machine learning algorithms
TL;DR: The results show that the constantly updated data obtained from the information layer together with the machine learning algorithms in the application layer can efficiently predict the future condition of MEP components for maintenance planning.
Journal ArticleDOI
Artificial Intelligence and Machine Learning Applications in Smart Production: Progress, Trends, and Directions
TL;DR: A literature review on ML and AI empirical studies published in the last century was carried out to highlight the evolution of the topic before and after Industry 4.0 introduction, from 1999 to now.
References
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LIBSVM: A library for support vector machines
Chih-Chung Chang,Chih-Jen Lin +1 more
TL;DR: Issues such as solving SVM optimization problems theoretical convergence multiclass classification probability estimates and parameter selection are discussed in detail.
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Deep learning in neural networks
TL;DR: This historical survey compactly summarizes relevant work, much of it from the previous millennium, review deep supervised learning, unsupervised learning, reinforcement learning & evolutionary computation, and indirect search for short programs encoding deep and large networks.
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A review on machinery diagnostics and prognostics implementing condition-based maintenance
TL;DR: This paper attempts to summarise and review the recent research and developments in diagnostics and prognostics of mechanical systems implementing CBM with emphasis on models, algorithms and technologies for data processing and maintenance decision-making.
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What is a support vector machine
TL;DR: Support vector machines are becoming popular in a wide variety of biological applications, but how do they work and what are their most promising applications in the life sciences?