Context Ontology Development for Connected Maintenance Services

Emmanouilidis, Christos; Gregori, Matteo; Al-Shdifat, Ali

doi:10.1016/j.ifacol.2020.12.2833

Cited by 6 publications

(2 citation statements)

References 24 publications

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“…Emmanouilidis et al proposed an ontology for maintenance where failure modes and effects criticality analysis can be performed [8]. This ontology is suitable for reliability analysis and might be reused, but no wide use or links to maintenance scheduling or any other services is considered in the actual version of the ontology.…”

Section: Related Workmentioning

confidence: 99%

Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot

Louadah,

Papadakis,

McCluskey

et al. 2024

Applied Sciences

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The railway industry forecasts growth in passenger and freight traffic over the next 30 years. This places additional demands on rolling stock depot facilities, many of which were designed and built before the modern age of information technology. This paper explores the potential of improving the efficiency and effectiveness of rolling stock maintenance management to meet the challenges of the near future, by utilising advanced computing techniques. The objective of the work is to create optimised maintenance plans for a fleet of trains, considering optimal use of resources. As a “glue” for joining up functions and operations, a generic Depot and Vehicle ontology (called the Virtual Depot) is introduced. The ontology captures the structures, relationships, and attributes of objects in the Depot (rolling stock, sensors, depot assets, tools, resources, and staff). The ontology is populated with example company and fleet-specific knowledge using an automated knowledge acquisition method. This paper describes the systematic method for the creation of a Virtual Depot. Two particular aspects are discussed in detail—knowledge acquisition of fleet-specific information obtained from a manufacturer’s Vehicle Maintenance Instruction manuals and the construction of a short-term scheduling process within the Virtual Depot. Our evaluation considers the integrative aspects of the method, demonstrating how the ontological structure and its acquired specific information informs and benefits the scheduling process, in particular with respect to schedule optimisation. Results from an initial case study show there is significant potential to optimise short-term maintenance schedules, and the ability to automatically consider resource availability in short-term scheduling is demonstrated.

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Section: Related Workmentioning

confidence: 99%

Supporting the Management of Rolling Stock Maintenance with an Ontology-Based Virtual Depot

Louadah,

Papadakis,

McCluskey

et al. 2024

Applied Sciences

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“…It constrains the classes that are unique to the maintenance management practice, such as maintenance strategy, degradation, and work order management. Emmanouilidis et al (2020) conducted a study of maintenance ontologies from the viewpoint of reliability-oriented context information management and proposes a baseline context information management ontology aligned with the needs of maintenance services for connected production machines. This ontology is applied on an industrial case study relevant to maintenance services for a distributed fleet of connected industrial printers.…”

Section: Related Workmentioning

confidence: 99%

Multiple aspects maintenance ontology-based intelligent maintenance optimization framework for safety-critical systems

Diao,

Zhao,

Vaddi

et al. 2024

AIEDAM

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Maintenance optimization is a process for improving the efficiency of maintenance strategies and activities, considering various aspects of the target system and components, such as the probabilities of system failures and the cost of repair and replacement of a failed component. The improvement of maintenance optimization algorithms generally requires information from various data sources. For example, it may require the system risk information derived from risk analysis tools or the residual lifetime of a component from fault prognosis tools. The requirements of data acquisition (DAQ) and aggregation pose new challenges for maintenance management systems (MMSs) that implement and use these maintenance optimization algorithms. This paper proposes a multiple aspects maintenance ontology-based framework to facilitate DAQ from MMSs, online monitoring systems, fault detection and discrimination tools, risk assessment tools, decision-making tools, and component identification tools, and accelerate the implementation and verification of contemporary maintenance optimization models and algorithms. The proposed framework consists of a multi-aspect maintenance ontology with critical information for maintenance optimization and application interfaces for collecting information from various data sources, such as fault prognosis tools, online monitoring tools, risk assessment tools, and decision-making algorithms. In addition, this paper proposes a heuristic method for integrating concepts and properties from other existing ontologies into the proposed framework when the existing ontology is not fully compatible with the ontology under construction. Finally, the paper verifies the proposed ontology framework using a feedwater system designed for nuclear power plants with valves and filters as the components under maintenance.

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