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Probing an intelligent predictive maintenance approach with deep learning and augmented reality for machine tools in IoT-enabled manufacturing
C Liu, H Zhu, D Tang, Q Nie, T Zhou, L Wang… - Robotics and Computer …, 2022 - Elsevier
Abstract In the Industry 4.0 era, the number and complexity of machine tools are both
increased, which is prone to cause malfunctions and downtime in the manufacturing …
increased, which is prone to cause malfunctions and downtime in the manufacturing …
Enhancing machine learning multi-class fault detection in electric motors through entropy-based analysis
In the field of electric motor maintenance, this study introduces a transformative approach by
integrating entropy-based algorithms with machine learning for enhanced multi-class fault …
integrating entropy-based algorithms with machine learning for enhanced multi-class fault …
An Industry 4.0 Oriented Predictive Maintenance Solution Deployed in Real-World Automotive Manufacturing Facilities
A Nicolae, C Burlacu, A Stanciu… - … on Control Systems …, 2023 - ieeexplore.ieee.org
Industry 4.0 drives a significant revolution in the industrial manufacturing area in recent
years, with predictive maintenance consolidating its potential to increase efficiency and …
years, with predictive maintenance consolidating its potential to increase efficiency and …
[HTML][HTML] Maintenance decision-making and its relevance in engineering asset management
S More, R Tuladhar, D Grainger, W Milne - Maintenance, Reliability and …, 2024 - extrica.com
Engineering asset management (EAM) has received a lot of attention in the last few
decades. Despite this, industries struggle to identify the best strategies for maintaining …
decades. Despite this, industries struggle to identify the best strategies for maintaining …
A Data-Driven Approach for Predictive Maintenance Integrated Production Scheduling
SX Zhai - 2023 - mediatum.ub.tum.de
This dissertation proposes an approach that integrates predictive maintenance models with
production and maintenance scheduling. A generative deep learning model was applied to …
production and maintenance scheduling. A generative deep learning model was applied to …