caa a22 4500 605519897 CHVBK 20210128100732.0 cr unu---uuuuu 210128e20151201xx s 000 0 eng 10.1007/s11009-014-9419-2 doi (NATIONALLICENCE)springer-10.1007/s11009-014-9419-2 Novák Petr Faculty of Mathematics and Physics, Department of Probability and Mathematical Statistics, Charles University in Prague, Sokolovská 83, 186 75, Praha 8, Czech Republic aut Regression Models for Repairable Systems [Elektronische Daten] [Petr Novák] When operating a device which is a subject to degradation, we want to estimate the distribution of the time to failure for maintenance optimization. Our aim is to describe the dependency of the failure time distribution on applicable regression variables. Models commonly used in survival analysis, such as the Cox model or the Accelerated failure time model, need to be adjusted to accommodate repairs and maintenance. For instance, we may use the number of repairs or maintenance actions or their cost as time-varying covariates. In this work we describe such models and demonstrate their application on real data. Springer Science+Business Media New York, 2014 Reliability analysis nationallicence Repair models nationallicence Regression nationallicence Methodology and Computing in Applied Probability Springer US; http://www.springer-ny.com 17/4(2015-12-01), 963-972 1387-5841 17:4<963 2015 17 11009 https://doi.org/10.1007/s11009-014-9419-2 text/html Onlinezugriff via DOI BK010053 XK010053 XK010000 Metadata rights reserved Springer special CC-BY-NC licence nationallicence 1 research-article jats NATIONALLICENCE NATIONALLICENCE NL-springer NATIONALLICENCE 856 40 https://doi.org/10.1007/s11009-014-9419-2 text/html Onlinezugriff via DOI NATIONALLICENCE 100 1- Novák Petr Faculty of Mathematics and Physics, Department of Probability and Mathematical Statistics, Charles University in Prague, Sokolovská 83, 186 75, Praha 8, Czech Republic aut NATIONALLICENCE 773 0- Methodology and Computing in Applied Probability Springer US; http://www.springer-ny.com 17/4(2015-12-01), 963-972 1387-5841 17:4<963 2015 17 11009