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Context awareness for maintenance decision making: A diagnosis and prognosis approach
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-4107-0991
Luleå University of Technology, Department of Civil, Environmental and Natural Resources Engineering, Operation, Maintenance and Acoustics.ORCID iD: 0000-0002-1938-0985
Department of Information Engineering, University of Florence.
Department of Information Engineering, University of Florence.
2015 (English)In: Measurement, ISSN 0263-2241, E-ISSN 1873-412X, Vol. 67, p. 137-150Article in journal (Refereed) Published
Abstract [en]

All assets necessarily suffer wear and tear during operation. Prognostics can assess the current health of a system and predict its remaining life based on features capturing the gradual degradation of its operational capabilities. Prognostics are critical to improve safety, plan successful work, schedule maintenance, and reduce maintenance costs and down time. Prognosis is a relatively new area but has become an important part of Condition-based Maintenance (CBM) of systems. Broadly stated, prognostic methods are either data-driven, rule based, or model-based. Each approach has advantages and disadvantages; consequently, they are often combined in hybrid applications. A hybrid model can combine some or all model types; thus, more complete information can be gathered, leading to more accurate recognition of the fault state. In this context, it is important to evaluate the consistency and reliability of the measurement data obtained during laboratory testing and the prognostic/diagnostic monitoring of the system under examination.This approach is especially relevant in systems where the maintainer and operator know some of the failure mechanisms with a sufficient amount of data, but the sheer complexity of the assets precludes the development of a complete model-based approach. This paper addresses the process of data aggregation into a contextual awareness hybrid model to get Residual Useful Life (RUL) values within logical confidence intervals so that the life cycle of assets can be managed and optimised.

Place, publisher, year, edition, pages
2015. Vol. 67, p. 137-150
National Category
Other Civil Engineering
Research subject
Operation and Maintenance
Identifiers
URN: urn:nbn:se:ltu:diva-12382DOI: 10.1016/j.measurement.2015.01.015ISI: 000351696900016Scopus ID: 2-s2.0-84925386908Local ID: b84d7d92-9f13-4b6f-977a-6d5ad73aad37OAI: oai:DiVA.org:ltu-12382DiVA, id: diva2:985332
Note
Validerad; 2015; Nivå 2; 20141121 (aditha)Available from: 2016-09-29 Created: 2016-09-29 Last updated: 2018-07-10Bibliographically approved

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Galar, DiegoThaduri, Adithya

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