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

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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.
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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.

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Predictive maintenance in the Industry 4.0: A systematic literature review

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.
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The strategic use of artificial intelligence in the digital era: Systematic literature review and future research directions

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

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.
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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

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?
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