Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study
This paper examines the integration of Industry 5.0 principles with advanced predictive maintenance (PdM) and condition monitoring (CM) practices, based on Industry 4.0's enabling technologies. It provides a comprehensive review of the roles of Machine Learning (ML), Digital Twins (DT), the Int...
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| Format: | Article |
| Language: | English |
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Elsevier
2024-12-01
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| Series: | Results in Engineering |
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| Online Access: | http://www.sciencedirect.com/science/article/pii/S2590123024011903 |
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| author | Aitzaz Ahmed Murtaza Amina Saher Muhammad Hamza Zafar Syed Kumayl Raza Moosavi Muhammad Faisal Aftab Filippo Sanfilippo |
| author_facet | Aitzaz Ahmed Murtaza Amina Saher Muhammad Hamza Zafar Syed Kumayl Raza Moosavi Muhammad Faisal Aftab Filippo Sanfilippo |
| author_sort | Aitzaz Ahmed Murtaza |
| collection | DOAJ |
| description | This paper examines the integration of Industry 5.0 principles with advanced predictive maintenance (PdM) and condition monitoring (CM) practices, based on Industry 4.0's enabling technologies. It provides a comprehensive review of the roles of Machine Learning (ML), Digital Twins (DT), the Internet of Things (IoT), and Big Data (BD) in transforming PdM and CM. The study proposes a six-layered framework designed to enhance sustainability, human-centricity, and resilience in industrial systems. This framework includes layers for data acquisition, processing, human-machine interfaces, maintenance execution, feedback, and resilience. A case study on a boiler feed-water pump is also presented which demonstrates the framework's potential benefits, such as reduced downtime, extended lifespan, real-time equipment monitoring and improved efficiency. The findings of this study emphasises the importance of integrating human intelligence with advanced technologies for a collaborative and adaptive industrial environment, and suggest areas for future research. |
| format | Article |
| id | doaj-art-dcae2453c7154c61a66acf4c98570e5f |
| institution | Kabale University |
| issn | 2590-1230 |
| language | English |
| publishDate | 2024-12-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Results in Engineering |
| spelling | doaj-art-dcae2453c7154c61a66acf4c98570e5f2024-12-19T10:57:28ZengElsevierResults in Engineering2590-12302024-12-0124102935Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case studyAitzaz Ahmed Murtaza0Amina Saher1Muhammad Hamza Zafar2Syed Kumayl Raza Moosavi3Muhammad Faisal Aftab4Filippo Sanfilippo5Department of Mechanical Engineering, Capital University of Science and Technology, Islamabad, 44000, PakistanSNS, National University of Sciences and Technology, Islamabad, 44000, PakistanDepartment of Engineering Sciences, University of Agder, Grimstad, 4879, NorwayDepartment of Engineering Sciences, University of Agder, Grimstad, 4879, NorwayDepartment of Engineering Sciences, University of Agder, Grimstad, 4879, NorwayDepartment of Engineering Sciences, University of Agder, Grimstad, 4879, Norway; Department of Software Engineering, Kaunas University of Technology, Kaunas, 51368, Lithuania; Corresponding author at: Department of Engineering Sciences, University of Agder, Grimstad, 4879, Norway.This paper examines the integration of Industry 5.0 principles with advanced predictive maintenance (PdM) and condition monitoring (CM) practices, based on Industry 4.0's enabling technologies. It provides a comprehensive review of the roles of Machine Learning (ML), Digital Twins (DT), the Internet of Things (IoT), and Big Data (BD) in transforming PdM and CM. The study proposes a six-layered framework designed to enhance sustainability, human-centricity, and resilience in industrial systems. This framework includes layers for data acquisition, processing, human-machine interfaces, maintenance execution, feedback, and resilience. A case study on a boiler feed-water pump is also presented which demonstrates the framework's potential benefits, such as reduced downtime, extended lifespan, real-time equipment monitoring and improved efficiency. The findings of this study emphasises the importance of integrating human intelligence with advanced technologies for a collaborative and adaptive industrial environment, and suggest areas for future research.http://www.sciencedirect.com/science/article/pii/S2590123024011903Industry 5.0Predictive maintenanceCondition monitoringDigital TwinsMachine LearningInternet of Things |
| spellingShingle | Aitzaz Ahmed Murtaza Amina Saher Muhammad Hamza Zafar Syed Kumayl Raza Moosavi Muhammad Faisal Aftab Filippo Sanfilippo Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study Results in Engineering Industry 5.0 Predictive maintenance Condition monitoring Digital Twins Machine Learning Internet of Things |
| title | Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study |
| title_full | Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study |
| title_fullStr | Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study |
| title_full_unstemmed | Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study |
| title_short | Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study |
| title_sort | paradigm shift for predictive maintenance and condition monitoring from industry 4 0 to industry 5 0 a systematic review challenges and case study |
| topic | Industry 5.0 Predictive maintenance Condition monitoring Digital Twins Machine Learning Internet of Things |
| url | http://www.sciencedirect.com/science/article/pii/S2590123024011903 |
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