Modeling the semantics of failure context as a means to offer context-adaptive maintenance support

Petros Pistofidis, Christos Emmanouilidis, Aggelos Papadopoulos, and Pantelis N Mpotsaris
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Full Paper
phmce_14_001.pdf1.73 MBJune 8, 2014 - 5:00am

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 contextually-relevant 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 maintenance-related knowledge.

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Submission Keywords: 
Context-adaptive maintenance support; semantics
knowledge management
Submission Topic Areas: 
Model-based methods for fault detection, diagnostics, and prognosis
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