| Title |
Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study / |
| Authors |
Ahmed Murtaza, Aitzaz ; Saher, Amina ; Hamza Zafar, Muhammad ; Kumayl Raza Moosavi, Syed ; Faisal Aftab, Muhammad ; Sanfilippo, Filippo |
| DOI |
10.1016/j.rineng.2024.102935 |
| Full Text |
|
| Is Part of |
Results in engineering.. Amsterdam : Elsevier. 2024, vol. 24, art. no. 102935, p. 1-24.. ISSN 2590-1230 |
| Keywords [eng] |
condition monitoring ; digital twins ; human-centric design ; Industry 5.0 ; internet of things ; machine learning ; predictive maintenance ; resilience ; sustainable industrial processes |
| Abstract [eng] |
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. |
| Published |
Amsterdam : Elsevier |
| Type |
Journal article |
| Language |
English |
| Publication date |
2024 |
| CC license |
|
