Intelligent decision support for maintenance: an overview and future trends. Issue 10 (3rd October 2019)

• Record Type: Journal Article
• Authors: Turner, C. J.; Emmanouilidis, C.; Tomiyama, T.; Tiwari, A.; Roy, R.
• Is Part Of: International journal of computer integrated manufacturing. Volume 32:Issue 10(2019)
• Page Start: 936
• Page End: 959
• Publication Date: 2019-10-03
• Subjects: Machine learning -- industry 4.0 -- E-maintenance -- intelligent maintenance; Computer integrated manufacturing systems -- Periodicals; 670.427
• DOI: 10.1080/0951192X.2019.1667033
• Languages: English
• ISSNs: 0951-192X

Abstract

ABSTRACT: The changing nature of manufacturing, in recent years, is evident in industry's willingness to adopt network-connected intelligent machines in their factory development plans. A number of joint corporate/government initiatives also describe and encourage the adoption of Artificial Intelligence (AI) in the operation and management of production lines. Machine learning will have a significant role to play in the delivery of automated and intelligently supported maintenance decision-making systems. While e-maintenance practice provides aframework for internet-connected operation of maintenance practice the advent of IoT has changed the scale of internetworking and new architectures and tools are needed. While advances in sensors and sensor fusion techniques have been significant in recent years, the possibilities brought by IoT create new challenges in the scale of data and its analysis. The development of audit trail style practice for the collection of data and the provision of acomprehensive framework for its processing, analysis and use should be avaluable contribution in addressing the new data analytics challenges for maintenance created by internet connected devices. This paper proposes that further research should be conducted into audit trail collection of maintenance data, allowing future systems to enable 'Human in the loop' interactions.