Modeling the Semantics of Failure Context as a means to offer Context-Adaptive Maintenance Support
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Abstract
Acting upon the data involved in typical diagnostics and prognostics tasks is often confounded by the complexity of the corresponding situation and needs to take into account domain-specific or even installation-specific knowledge considerations. While domain knowledge is often captured in various forms, such as is typically done in Fault Modes, Effects and Criticality Analysis (FMECA), the contextualisation of the captured data and related knowledge to a corresponding situation, in other words a situated-aware modeling of data and knowledge, is often missing. Our research leverages the efficiency of maintenance support for mobile actors. Investing in modern service provision technologies, this work targets the effectiveness of capturing and sharing field expertise. An analysis of both the modeling specification and the functional requirements for such an approach. is provided. The semantics of “Failure Context”, a context that guides user’s navigation towards relevant diagnostics and maintenance-related knowledge, are mapped into an appropriate data schema. Based on this, a system capable of managing the core information of the Failure Context, while offering adequate tools that support experts to build on, browse through, and reach contextuallyrelevant decisions is implemented. The development follows a reference-annotation design pattern to deliver on spot capture and enrichment of maintenance-related knowledge. Thus, the developed system provides the means for the effective management and exploitation of 'micro-knowledge fragments', associated with FMECA-related entities and knowledge. This is a significant enabler for the effective elicitation and management of field-captured expertise, enabling the enrichment and validation of maintenancerelated knowledge.
How to Cite
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Context-adaptive maintenance support, semantics, knowledge management
Cannata, A., Karnouskos, S., & Taisch, M. (2010). Dynamic e-maintenance in the era of SOA-ready device dominated industrial environments Engineering Asset Lifecycle Management (pp. 411-419): Springer.
Emmanouilidis, C., Liyanage, J. P., & Jantunen, E. (2009). Mobile solutions for engineering asset and maintenance management. Journal of Quality in Maintenance Engineering, 15(1), 92-105.
Emmanouilidis, C., & Pistofidis, P. (2010a). Machinery selfawareness with wireless sensor networks: a means to sustainable operation. Proceedings of the 2nd workshop on maintenance for sustainable manufacturing, 12 May 2010, Verona, Italy.
Emmanouilidis, C., & Pistofidis, P. (2010b). Wireless condition monitoring and embedded novelty detection Definitions, Concepts and Scope of Engineering Asset Management (pp. 195-238): Springer.
Lee, J., Lapira, E., Bagheri, B., & Kao, H.-a. (2013). Recent advances and trends in predictive manufacturing systems in big data environment. Manufacturing Letters, 1(1), 38-41.
Liyanage, J. P., Lee, J., Emmanouilidis, C., & Ni, J. (2009). Integrated e-Maintenance and intelligent maintenance systems Handbook of maintenance management and engineering (pp. 499-544): Springer.
Mouzoune, A., & Taibi, S. (2013). Towards an intelligence based conceptual framework for e-maintenance. Proceeding of 8th International Conference on Intelligent Systems: Theories and Applications (SITA), 8-9 May 2013, Rabat, Morocco.
Mouzoune, A., & Taibi, S. (2014). Introducing Emaintenance 2.0. International Journal of Computer Science and Business Informatics, 9(1).
Nadoveza, D., & Kiritsis, D. (2013). Concept for Context-Aware Manufacturing Dashboard Applications. 7th IFAC Conference on Manufacturing Modelling,Management, and Control, 19-21 June 2013,
Saint Petersburg, Russia.
Pistofidis, P., & Emmanouilidis, C. (2012). Developing Advanced Context Aware Tools for Mobile Maintenance. Proc. of the 2nd IFAC workshop on Advanced Maintenance Engineering, Services and
Technology, 22-23 November 2012, Seville, Spain.
Pistofidis, P., & Emmanouilidis, C. (2013). Profiling context awareness in mobile and cloud based engineering asset management Advances in Production Management Systems. Competitive Manufacturing for Innovative Products and Services (pp. 17-24): Springer.
Pistofidis, P., Emmanouilidis, C., Koulamas, C., Karampatzakis, D., & Papathanassiou, N. (2012). A layered e-maintenance architecture powered by smart wireless monitoring components. Proc. of the IEEE International Conference on Industrial Technology (ICIT), , 19-21 March 2011, Athens, Greece.
Savino, M. M., Brun, A., & Riccio, C. (2011). Integrated system for maintenance and safety management through FMECA principles and fuzzy inference engine. European Journal of Industrial Engineering, 5(2), 132-169.
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