Ontology-Based Context Modeling in Physical Asset Integrity Management

Abstract

Asset management is concerned with the management practices, technologies and tools necessary to maximize the value delivered by physical engineering assets. IoT-generated data are increasingly considered as an asset and the data asset value needs to be maximized too. However, asset-generated data in practice are often collected in non-actionable form. Collected data may comprise a wide number of parameters, over long periods of time and be of significant scale. Yet they may fail to represent the range of possible scenarios of asset operation or the causal relationships between the monitored parameters, and so the size of the data collection, while adding to the complexity of the problem, does not necessarily allow direct data asset value exploitation. One way to handle data complexity is to introduce context information modeling and management, wherein data and service delivery are determined upon resolving the apparent context of a service or data request. The aim of the present paper is, therefore, 2-fold: to analyse current approaches to addressing IoT context information management, mapping how context-aware computing addresses key challenges and supports the delivery of monitoring solutions; and to develop a maintenance context ontology focused on failure analysis of mechanical components so as to drive monitoring services adaptation. The approach is demonstrated by applying the ontology on an industrially relevant physical gearbox test rig, designed to model complex misalignment cases met in manufacturing and aerospace applications.

Circular production and maintenance of automotive parts: An Internet of Things (IoT) data framework and practice review

“…In addressing this shortfall of human and digital maintenance system integration Brundage et al (2019) suggest that artificial intelligence may provide a level of adaptation to bridge the gap between human and assistive maintenance tools. The use of ontology in combination with context awareness Al-Shdifat et al (2020) state that maintenance data in itself should be valued Turner et al (2021a) and Emmanouilidis et al (2019) set out the case for human in the loop systems, where artificial intelligence is used to aid man-machine interactivity and cooperation in the completion of industrial tasks; a direction also envisaged in the form of human centric industrial systems described in the emerging Industry 5.0 agenda (Breque et al, 2021).…”