Condition monitoring (CM) is a critical component of industrial asset maintenance and management, particularly in the context of manufacturing. It 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 condition monitoring 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 an Innovate UK-funded project, an intelligent condition monitoring system (called JANUS) was designed and developed in the R&D division of Monition Limited (now RS Components Limited) in order to contribute to operational efficiency, not only by means of reducing asset downtime via more accurate prediction of asset health condition but by more efficient use of technicians/labour resources. In order to meet these objectives, JANUS used machine learning and decision-making algorithms together to form an AI-DSS-based platform for analysis of condition monitoring data.