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Machine Learning for Industry 4.0: A Systematic Review Using Deep Learning-Based Topic Modelling.

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  • 1Department of Computer Science, University of Pisa, Largo B. Pontecorvo 3, 56127 Pisa, Italy.

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|November 26, 2022
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Summary
This summary is machine-generated.

This review analyzes machine learning (ML) in Industry 4.0, finding security and predictive maintenance are key topics. While Convolutional Neural Networks (CNNs) dominate, industry prioritizes ML adoption over model improvement.

Keywords:
deep learningindustry 4.0machine learningsystematic reviewtopic modelling

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Area of Science:

  • Industrial Automation and Machine Learning
  • Data Science and Big Data Analytics
  • Industry 4.0 Technologies

Background:

  • Industry 4.0 represents a paradigm shift in industrial processes, leveraging smart connectivity and automation.
  • Machine learning (ML) offers scalable predictive power crucial for addressing complex industrial challenges.
  • A comprehensive understanding of the intersection between ML and Industry 4.0 is hindered by the vast volume of research.

Approach:

  • A systematic literature review was conducted, analyzing 45,783 papers from Scopus and Web of Science.
  • BERTopic modeling was employed to identify and analyze key topics within the ML and Industry 4.0 landscape.
  • A comparative analysis included a manual review of 17 industry white papers to contrast academic focus with industry perspectives.

Key Points:

  • Security and predictive maintenance emerged as the most prominent application areas for ML in Industry 4.0.
  • Convolutional Neural Networks (CNNs) were identified as the most frequently utilized ML method in the reviewed literature.
  • Industry practitioners currently emphasize facilitating ML adoption over the development of advanced ML models.

Conclusions:

  • Academic research effectively highlights relevant ML applications within Industry 4.0.
  • There is a need for increased focus on technologies that simplify and enhance the adoption of ML in industrial settings.
  • Bridging the gap between academic ML advancements and practical industrial implementation requires further attention.