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Establishing a data farm to harvest quality reliability information
Author(s) -
Thomas Harold Hal W,
Moosemiller Mike
Publication year - 2001
Publication title -
process safety progress
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.378
H-Index - 40
eISSN - 1547-5913
pISSN - 1066-8527
DOI - 10.1002/prs.680200305
Subject(s) - reliability (semiconductor) , quality (philosophy) , data quality , computer science , reliability engineering , data reliability , work (physics) , data mining , risk analysis (engineering) , engineering , operations management , business , mechanical engineering , metric (unit) , philosophy , power (physics) , physics , epistemology , quantum mechanics
Abstract Obtaining useful reliability information from collected maintenance and inspection data requires effort. In the past, this has been referred to as data “mining,” as if the data can be extracted in its desired form if only it can be found. In contrast, this paper proposes data “farming,” and describes the “seeds” that are necessary to harvest the best possible crop of reliability information. The CCPS Equipment Reliability Database project provides valuable lessons on how to “farm” rather than merely “mine” data. The CCPS work processes for establishing failure modes, populations to track, event data to collect, and implementation are all reviewed. Attention is given to knowing up front the data objectives and the quality of information desired. Also, the treatment of equipment surveillance periods turns out to be a critical variable for data quantity and quality. Reasons for this and approaches to take are discussed. It will be seen that the quality and continuity of derived information is much greater when the data sources can be “farmed” rather than “mined”.