Special Issue: Digital Technologies-Driven Intelligent Maintenance Systems and Asset Management

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Special Issue Information

With continued global market growth and an increasingly competitive environment, industry and manufacturing are facing challenges and desires to seek continuous improvement. This effect is forcing industrial manufacturers to squeeze every asset for maximum value and thereby calls for high-equipment effectiveness, and at the same time flexible and resilient industry and manufacturing systems. Maintenance systems are essential to modern industrial manufacturing systems in terms of minimizing unplanned downtime, assuring product quality, reducing customer dissatisfaction, and maintaining advantages and competitiveness edge in the market.
We are living in a digital era. It is evident that the practice of maintenance and asset management has the potential to be one of the biggest beneficiaries of this digital revolution. Industry 4.0 technologies such as the Industrial Internet of Things, AI and Machine Learning, Cloud and Edge Computing, Smart Sensors, Digital Twins and Augmented Reality are creating new impetus for intelligent maintenance systems and smart asset lifecycle management, which are seen as a major opportunity for companies in the manufacturing and infrastructure sectors to improve their products, processes and services. This, in turn, drives businesses and organizations to adopt innovative business and service models closely linked to data-driven value creation approaches that leverage descriptive, predictive, and prescriptive analytics to protect the resiliency and integrity of industrial systems and maximize the value extracted from assets across their lifecycle. This value-adding activity is not only relevant for new smart assets, but is also highly applicable to "smart retrofitted" assets during their lifecycle, due to the increasing availability and penetration of such technologies and their lower costs.
The goal of this special issue is to bring together scholars from academia and industry to discuss recent advances in digital technologies and their implications for reliability, maintenance systems, and asset management. This special issue will cover a broad range of research and application topics, exploring the role of data-driven maintenance systems and asset management in manufacturing and industry. Topics of interest include, but are not limited to:


1.Predictive maintenance using artificial intelligence, deep learning and machine learning
2.Industry 4.0 technologies for predictive maintenance: Digital Twins, Industrial Internet of Things, Artificial Intelligence, Machine Learning, Cloud and Edge Computing, Smart Sensors
3.Integrated maintenance and production systems
4.Simulation and Optimisation in Maintenance
5.Data-driven Decision-making
6.Condition-based Maintenance
7.Business Models for Maintenance Services
8.Asset lifecycle management
9.AI for reliability and resilience of complex systems maintenance
10.Condition monitoring, diagnosis and prediction
11.Equipment management and maintenance
12.Intelligent maintenance system and E-maintenance

Special Issue Editors

Guest Editors:

Prof. Zhenling Liu

Affiliations: Henan University of Technology


Submission deadline: 31 December, 2022
Notification to authors: 31 March, 2023
Final versions due by: 31 May, 2023