Differential Importance Measure for Components Subjected to Aging Phenomena
Author(s) -
Stefano La Rovere,
Paolo Vestrucci,
Maria Sperandii,
Claudia Mandurino
Publication year - 2013
Publication title -
journal of quality and reliability engineering
Language(s) - English
Resource type - Journals
eISSN - 2314-8047
pISSN - 2314-8055
DOI - 10.1155/2013/945039
Subject(s) - unavailability , maintainability , reliability engineering , preventive maintenance , reliability (semiconductor) , measure (data warehouse) , weibull distribution , correctness , computer science , ranking (information retrieval) , exponential function , failure rate , exponential distribution , process (computing) , spare part , component (thermodynamics) , mathematical optimization , mathematics , statistics , algorithm , engineering , data mining , artificial intelligence , mechanical engineering , mathematical analysis , power (physics) , physics , thermodynamics , quantum mechanics , operating system
The paper refers to the evaluation of the unavailability of systems made by repairable binary independent components subjected to aging phenomena. Exponential, exponential-linear, and Weibull distributions are assumed for the components failure times. We assume that components failure rate increases only slightly during the maintenance period, but we recognize the effectiveness of preventive maintenance only in presence of aging phenomena. Importance measures allow the ranking of the input variables. We propose analytical equations that allow the estimation of the first-order Differential Importance Measure (DIM) on the basis of the Birnbaum measures of components, under the hypothesis of uniform percentage changes of parameters. Without further information than that used for the estimation of “DIM for components,” “DIM for parameters” allows considering separately the importance of random failures, aging phenomena, and preventive and corrective maintenance. A two-step process is proposed for the system improvement, by increasing the components reliability and maintainability performance as much as possible (within the applicable technological limits) and then by optimizing preventive maintenance on them. Some examples taken from the scientific literature are solved in order to verify the correctness of the analytical equations and to show their use
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