Change search
ReferencesLink to record
Permanent link

Direct link
Context Driven Remaining Useful Life Estimation
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.
2014 (English)Conference paper, Meeting abstract (Refereed)
Abstract [en]

In the context of maintenance activities maintainers rely on machine information, their past breakdowns, adequate repair methods and guidelines as well as new research results in the area. They usually get access to information and knowledge by using information systems (nondestructive testing (NDT) or condition monitoring.), local databases, e-resources or traditional print media. Basically it can be assumed that, the amount of available information affects the quality of maintenance decision making and acting positively. Machine health information retrieval is the application of information retrieval concepts and techniques to the operation and maintenance domain. Retrieving Contextual information, describing the operational conditions for the machine, is a subarea of information retrieval that incorporates context features in the search process towards its improvement. Both areas have been gaining interest from the research community in order to perform more accurate prognostics according to specific scenarios and happening circumstances. Context is a broad term and in this paper the operational conditions and the way the machine has been used is seen as the context and is represented by operational data collected over time. This paper intends to investigate the effects of the interaction of context features on machine tools health information. This interaction between context and health assessment is bidirectional in the sense that health information seeking behavior can also be used to predict context features that can be used, without disturbing the operational environment and creating production disruptions.The extraction of multiple features from multiple sensors, already deployed in this type of machinery, may constitute snapshots of the current health of certain machine components. The mutation status (the way they have changed) of these snapshots, hereafter called Fingerprints, has been proposed as prognostic marker in machine tools problems. Of them, in this work so far only the spindle fingerprint mutation has been validated independently as prognostic for overall survival and survival after relapse, while the prognostic value of rest of components mutation is still under validation. In this scenario, the prognostic value of spindle fingerprint mutations can be investigated in various contexts defined by stratifications of the machine population.

Place, publisher, year, edition, pages
2014. Vol. 22, 181–185- p.
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-39494DOI: 10.1016/j.procir.2014.07.129Local ID: e46b4511-2d95-43c3-a95e-9c9ab4be4731OAI: oai:DiVA.org:ltu-39494DiVA: diva2:1013007
Conference
International Conference in Through-life Engineering Services : 3rd International Conference On Through-life Engineering Services 04/11/2014 - 05/11/2014
Note
Godkänd; 2014; 20141104 (andbra)Available from: 2016-10-03 Created: 2016-10-03Bibliographically approved

Open Access in DiVA

No full text

Other links

Publisher's full text

Search in DiVA

By author/editor
Johansson, Carl-AndersSimon, VictorGalar, Diego
By organisation
Operation, Maintenance and Acoustics

Search outside of DiVA

GoogleGoogle Scholar

Altmetric score

Total: 6 hits
ReferencesLink to record
Permanent link

Direct link