NDLI: Extraction and exploitation of R&M knowledge from a fleet perspective

Content Provider	IEEE Xplore Digital Library
Author	Subbiah, S. Turrin, S.
Copyright Year	2015
Description	Author affiliation: Ind. Software & Applic., Life Cycle Sci, ABB Corp. Res. Center Germany, Ladenburg, Germany (Subbiah, S.; Turrin, S.)
Abstract	The availability of failure data, condition monitoring data and operational data for large sets of homogeneous products (in the following, referred to as a fleet) motivates the development of new analytical methods to extract and exploit reliability and maintainability knowledge on a fleet level. In other words, by clustering products into fleets based on categories of interest (e.g. subsets of assets belonging to the same customer or installed in the same region or operating for the same industrial application) the fleet-specific usage and degradation profiles can be extracted by statistically analyzing the operational, monitoring and failure data of the assets belonging to the selected fleets. In this paper we describe the process of extracting reliability knowledge (in terms of usage and degradation profiles) at the single product level and at the fleet level. The extracted knowledge is then exploited to determine the remaining useful life of a unit in the fleet. In this context, a simple but an effective data-driven prognostic algorithm is proposed. The algorithm makes use of the knowledge extracted at a fleet level to enhance its forecasting capabilities.
Starting Page	1
Ending Page	6
File Size	235121
Page Count	6
File Format	PDF
e-ISBN	9781479967032
DOI	10.1109/RAMS.2015.7105072
Language	English
Publisher	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Publisher Date	2015-01-26
Publisher Place	USA
Access Restriction	Subscribed
Rights Holder	Institute of Electrical and Electronics Engineers, Inc. (IEEE)
Subject Keyword	Data mining Degradation Reliability Condition monitoring Monitoring Power system reliability predictive maintenance prognostics condition monitoring statistical analysis
Content Type	Text
Resource Type	Article

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