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Article

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Title

ML-Based Maintenance and Control Process Analysis, Simulation, and Automation - A Review

Authors

[ 1 ] Instytut Technologii Materiałów, Wydział Inżynierii Mechanicznej, Politechnika Poznańska | [ P ] employee

Scientific discipline (Law 2.0)

[2.9] Mechanical engineering

Year of publication

2024

Published in

Applied Sciences

Journal year: 2024 | Journal volume: vol. 14 | Journal number: iss. 19

Article type

scientific article

Publication language

english

Keywords
EN
  • machine learning
  • deep learning
  • Industry 4.0
  • Industry 5.0
  • control
  • signal classification
  • EEG
  • BCI
Abstract

EN Automation and digitalization in various industries towards the Industry 4.0/5.0 paradigms are rapidly progressing thanks to the use of sensors, Industrial Internet of Things (IIoT), and advanced fifth generation (5G) and sixth generation (6G) mobile networks supported by simulation and automation of processes using artificial intelligence (AI) and machine learning (ML). Ensuring the continuity of operations under different conditions is becoming a key factor. One of the most frequently requested solutions is currently predictive maintenance, i.e., the simulation and automation of maintenance processes based on ML. This article aims to extract the main trends in the area of ML-based predictive maintenance present in studies and publications, critically evaluate and compare them, and define priorities for their research and development based on our own experience and a literature review. We provide examples of how BCI-controlled predictive maintenance due to brain–computer interfaces (BCIs) play a transformative role in AI-based predictive maintenance, enabling direct human interaction with complex systems.

Date of online publication

28.09.2024

Pages (from - to)

8774-1 - 8774-29

DOI

10.3390/app14198774

URL

https://www.mdpi.com/2076-3417/14/19/8774

Comments

Article Number: 8774

License type

CC BY (attribution alone)

Open Access Mode

open journal

Open Access Text Version

final published version

Date of Open Access to the publication

at the time of publication

Ministry points / journal

100

Impact Factor

2,5 [List 2023]

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