Browsing by Author "Al-Shdifat, Ali"
Now showing 1 - 3 of 3
Results Per Page
Sort Options
Item Open Access Context ontology development for connected maintenance services(Elsevier, 2020-11-30) Emmanouilidis, Christos; Gregori, Matteo; Al-Shdifat, AliThe opportunity to shift from corrective and preventive to data-driven Predictive Maintenance has received a significant boost with the deeper penetration of Internet of Things (IoT) technologies in industrial environments. Processing IoT generated data nonetheless creates challenges for data management and actionable data processing. One way to handle such complexity is to introduce context information modelling and management, wherein data and service delivery are determined upon resolving the apparent context of a service or data request. In this paper, context information management is considered on the basis of a valid knowledge construct for reliability-oriented maintenance management. The aim is to produce a viable semantic organization of data for maintenance services. It is applied on an industrial case linked to maintenance of a distributed fleet of connected production grade industrial printers. The complexity of translating the data generated by such production assets to actionable information is significant, as the status of a single asset is characterised by several hundreds of failure modes and a multitude of event codes. To assess the viability of the ontology for the targeted application, a qualitative usability evaluation study of the ontology is performed.Item Open Access Ontology – based context resolution in internet of things enabled diagnostics(Elsevier, 2020-12-18) Al-Shdifat, Ali; Emmanouilidis, Christos; Khan, Muhammad; Starr, AndrewInternet of things (IoT)-generated data from industrial systems are often collected in non-actionable form, thus not directly aiding maintenance actions. Context information management is often seen as an enabler for interoperability and context-based service adaptation, acting as a mechanism for linking data with knowledge to adaptive data and services. Ontology-based approaches for semantic maintenance have been proposed in the past as a data and service mediation mechanism and are adopted here as the starting point employed to develop a context resolution service for industrial diagnostics. The underlying ontology of the context resolution mechanism is relevant to failure analysis of mechanical components. The terminology and relationship between concepts are structured on the basis of relevant standards with a reliability-oriented knowledge grounding. A reasoning mechanism is employed to deliver context resolution and the derived context can add a metadata layer on data or events generated by automated and human-driven means. The approach is applied on a gearbox test rig appropriate for emulating complex misalignment cases met in many manufacturing and aerospace applicationsItem Open Access Ontology-based context modeling in physical asset integrity management(Frontiers Research, 2020-10-30) Al-Shdifat, Ali; Emmanouilidis, Christos; Khan, Muhammad; Starr, Andrew G.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.