Paradigm shift for predictive maintenance and condition monitoring from Industry 4.0 to Industry 5.0: A systematic review, challenges and case study
Year: 2024
Type: review
Abstract: 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. • Research fills literature gap by integrating ML, DT, IoT, BD for PdM and CM in Industry 5.0. • Comprehensive review of technologies for RUL prediction and machine health monitoring. • Case study on boiler feed-water pump employs core tech for PdM, aligning with Industry 5.0. • Study discusses challenges in PdM and CM application, proposing future solution pathways. • Offers practical guidance for implementing PdM and CM, aiding academic and industry progress.
Source: Results in Engineering
Language: English
Subfield: Industrial and Manufacturing Engineering
Field: Engineering
Domain: Physical Sciences
Open Access status: gold
Estimated APC paid: $1,490
Funder: Universitetet i Agder
OpenAlex ID: W4402738398