[1A2] Machine learning-enabled decision support system (ML-DSS) for asset condition monitoring
A Rahbarimanesh¹, M Burrows² and S Rahbarimanesh³
¹University of Manchester, UK
²RS Group plc, UK
³University of British Columbia, Canada
Condition monitoring (CM) is a critical component of industrial asset maintenance and management, particularly in the manufacturing context. CM identifies significant changes in a piece of machinery’s performance which could be indicative of a developing fault and potentially lead to significant operational cost and even major disruption in manufacturing and production.
Implementation of CM in a typical industrial environment requires support by a system of interconnected software and hardware elements. Traditionally, these systems were developed merely for the specific task of asset health monitoring. However, the digitalisation wave of Industry 4.0 and wider application of artificial intelligence-based (smart) technologies has provided a great opportunity for further development of these systems, thereby making substantial contributions to the efficiency of manufacturing and production.
As a part of a UK Government (InnovateUK)-funded project, an intelligent condition monitoring system (called JANUS) was designed and developed in the research and development (R&D) division of Monition Limited (now RS Group plc) in order to contribute to operational efficiency not only by means of reducing asset downtime via more accurate and on-time prediction of asset health condition but more efficient use of technicians/labour resources. In order to meet these objectives, JANUS used supervised learning-based machine learning (ML) algorithms along with multi-criteria decision-making techniques to develop an ML-enabled decision support system for analysis of asset condition monitoring data.
Implementation of CM in a typical industrial environment requires support by a system of interconnected software and hardware elements. Traditionally, these systems were developed merely for the specific task of asset health monitoring. However, the digitalisation wave of Industry 4.0 and wider application of artificial intelligence-based (smart) technologies has provided a great opportunity for further development of these systems, thereby making substantial contributions to the efficiency of manufacturing and production.
As a part of a UK Government (InnovateUK)-funded project, an intelligent condition monitoring system (called JANUS) was designed and developed in the research and development (R&D) division of Monition Limited (now RS Group plc) in order to contribute to operational efficiency not only by means of reducing asset downtime via more accurate and on-time prediction of asset health condition but more efficient use of technicians/labour resources. In order to meet these objectives, JANUS used supervised learning-based machine learning (ML) algorithms along with multi-criteria decision-making techniques to develop an ML-enabled decision support system for analysis of asset condition monitoring data.
