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

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 o...

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Bibliographic Details
Published in:Applied sciences 2024-10, Vol.14 (19), p.8774
Main Authors: Rojek, Izabela, Mikołajewski, Dariusz, Dostatni, Ewa, Piszcz, Adrianna, Galas, Krzysztof
Format: Article
Language:English
Subjects:
Algorithms
Analysis
Artificial intelligence
Automation
Brain research
Classification
Communication
control
Convulsions & seizures
Decision making
deep learning
Disease
Electrodes
Epilepsy
Human performance
Industry 4.0
Industry 5.0
Machine learning
Neurological disorders
R&D
Research & development
Rhythm
signal classification
Signal processing
Simulation
Sleep
Technology application
Trends
Citations: Items that this one cites
Online Access:Get full text
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Summary: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.
ISSN:2076-3417
2076-3417
DOI:10.3390/app14198774