NDLI: Parameter estimation in degradation modelling: A case study using condition monitoring data from wood pole inspections

Content Provider	IEEE Xplore Digital Library
Author	Welte, T.M. Kile, H.
Copyright Year	2011
Description	Author affiliation: Department of Energy Systems, SINTEF Energy Research, 7465 Trondheim, Norway (Welte, T.M.; Kile, H.)
Abstract	Estimating model parameters is a major challenge in degradation modelling. This paper presents an approach where sojourn time distribution parameters were estimated based on censored observations from condition monitoring. A maximum likelihood approach for parameter estimation is described, and the requirements related to type and amount of data are discussed. It is shown that feasible parameter estimates can be obtained when sufficient amount of data is available, even though the data is heavily censored. A case study is presented where data from wood pole inspections were applied for parameter estimation. The data were collected by a Norwegian electricity distribution company during condition monitoring of their power lines. The case study showed that parameter estimates can be established based on such type of data. However, the case study also revealed challenges and limitations because of missing or inconsistent information in the database. The paper discusses these aspects. Furthermore, suggestions on improvement of data collection and parameter estimation are given.
Starting Page	1
Ending Page	7
File Size	294566
Page Count	7
File Format	PDF
ISBN	9781424484195
e-ISBN	9781424484188
e-ISBN	9781424484171
DOI	10.1109/PTC.2011.6019168
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2011-06-19
Publisher Place	Norway
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Inspection Condition monitoring Time series analysis Parameter estimation Poles and towers Degradation Maximum likelihood estimation sojourn time Deterioration inspection Markov processes parameter estimation
Content Type	Text
Resource Type	Article

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