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Prediction of time for preventive maintenance
KTH, School of Electrical Engineering (EES), Electromagnetic Engineering. (RCAM)ORCID iD: 0000-0002-2462-8340
KTH, School of Electrical Engineering (EES), Electromagnetic Engineering. (RCAM)ORCID iD: 0000-0002-2964-7233
2016 (English)In: The Nordic Conference in Mathematical Statistics, Köpenhamn, 2016Conference paper, Poster (with or without abstract) (Refereed)
##### Abstract [en]

In maintenance planning a crucial question is when some asset should be maintained. As preventive maintenance often needs outages it is necessary to predict the condition of an asset until the next possible outage. The degradation of the condition can be modelled by a linear function. One method of estimating the condition is linear regression, which requires a number of measured values for different times and gives an interval within which the asset will reach a condition when it should be maintained [1]. A more sophisticated calculation of the uncertainty of the regression is presented based on [2, section 9.1].

Another method is martingale theory [3, chapter 24], which serves to deduce a formula for the time such that there is a probability of less than a given $\alpha$ that the condition has reached 0 before that time. The formula contains an integral, which is evaluated numerically for different values of the measurement variance and the variance of the Brownian motion, which must be estimated by knowing the maximum and the minimum degradation per time interval. Then just one measured value is needed together with an estimate of the variance.

The two methods are compared, especially with regard to the size of the confidence interval of the time when the condition reaches a predefined level. The application for the methods is the development of so called health indices for the assets in an engineering system, which should tell which asset need maintenance first. We present some requirements for a health index and check how the different predictions fulfil these requirements.

References

[1] S.E. Rudd, V.M. Catterson, S.D.J. McArthur, and C. Johnstone. Circuit breaker prognostics using SF6 data. In IEEE Power and Energy Society General Meeting, Detroit, MI, United States, 2011.

[2] Bernard W. Lindgren. Statistical theory. Macmillan, New York, 2nd edition, 1968.

[3] Jean Jacod and Philip Protter. Probability essentials. Springer-Verlag, Berlin, 2000.

Köpenhamn, 2016.
##### Keyword [en]
linear regression, martingale theory, health index, maintenance planning
##### Keyword [sv]
linjär regression, martingalteori, hälsoindex, underhållsplanering
##### National Category
Probability Theory and Statistics
##### Research subject
Electrical Engineering
##### Identifiers
OAI: oai:DiVA.org:kth-193910DiVA, id: diva2:1034456
##### Conference
Nordstat 2016,26th Nordic Conference in Mathematical Statistics, June 27 - 30, Denmark
##### Funder
SweGRIDS - Swedish Centre for Smart Grids and Energy Storage
##### Note

QC 20161013

Available from: 2016-10-12 Created: 2016-10-12 Last updated: 2017-10-03Bibliographically approved

#### Open Access in DiVA

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##### By author/editor
Westerlund, PerHilber, Patrik
##### By organisation
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Probability Theory and Statistics

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Cite
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