ML-Based Maintenance and Control Process Analysis, Simulation, and Automation—A Review
Source abstract
Abstract
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.
Source: crossref · Quality: baseline
Research context
OpenAlex topics: Fault Detection and Control Systems · Industrial Vision Systems and Defect Detection · Manufacturing Process and Optimization
SciVX discovery tags are terminology-based. OpenAlex topics are source-provided classifications.
Affiliations
Faculty of Computer Science, Kazimierz Wielki University, Chodkiewicza 30, 85-064 Bydgoszcz, Poland
Faculty of Mechanical Engineering, Poznań University of Technology, Marii Skłodowskiej-Curie 5, 60-965 Poznan, Poland
Funding
Polish Minister of Science and Higher Education
References (86)
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