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Reliability estimation based on operational data of manufacturing systems
Author(s) -
Li Lin,
Ni Jun
Publication year - 2008
Publication title -
quality and reliability engineering international
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.913
H-Index - 62
eISSN - 1099-1638
pISSN - 0748-8017
DOI - 10.1002/qre.959
Subject(s) - reliability engineering , reliability (semiconductor) , weibull distribution , preventive maintenance , parametric statistics , estimation , computer science , imperfect , automotive industry , work (physics) , engineering , statistics , mathematics , systems engineering , power (physics) , mechanical engineering , physics , linguistics , philosophy , quantum mechanics , aerospace engineering
Maintenance management has a direct influence on equipment reliability and safety. However, a large portion of traditional maintenance models and reliability analysis methods usually assumes that only perfect maintenance is performed on the system and the system will restore to as good as new regardless of the kind of preventive maintenance work‐order that is performed. This is not practical in reality and may result in an inaccurate parametric estimation. The research objective of this paper is to develop a maximum likelihood estimation method to obtain more accurately estimated parameters based on the operational data of manufacturing systems, taking into consideration the difference between perfect and imperfect maintenance work‐orders. Weibull distribution is specifically studied for this purpose. A practical case study based on industrial operational data from an automotive assembly line is performed to illustrate the implementation and efficiency of the proposed reliability estimation method. Copyright © 2008 John Wiley & Sons, Ltd.