Soft computing approaches in reliability modeling and analysis of repairable systems

• Published in: 2010 Proceedings - Annual Reliability and Maintainability Symposium (RAMS) pp. 1 - 5
• Main Authors: Salgado, Marcia F P, Caminhas, Walmir M, Menezes, Benjamim R
• Format: Conference Proceeding
• Language: English
• Published: IEEE 01.01.2010
• Subjects: Artificial neural networks; Computational intelligence; Computer applications; Computer networks; Context modeling; Failure analysis; Fuzzy systems; maintainability; Maintenance engineering; Power engineering computing; reliability; Reliability engineering; repairable systems; soft computing
• ISBN: 9781424451029; 1424451027
• ISSN: 0149-144X
• DOI: 10.1109/RAMS.2010.5447986

This paper reviews soft computing approaches for reliability modeling and analysis of repairable systems. Although soft computing techniques such as neural networks and fuzzy systems and even stochastic methods have been employed for solving many different engineering complex problems, when it comes to reliability area traditional approaches are still preferred by industry. Unfortunately with the increasing complexity of systems such techniques might not be able to capture the changes in system features in a precise way what could help to prevent failures and improve system performance. This is a fairly new research area and the literature available points to the new challenges reliability engineers will have to face and the new tools they might use for planning and improving system reliability. In this paper basics of soft computing techniques will be provided as well as examples on how to apply them on the modeling and analysis of repairable systems. It is emphasized that this is a broad open subject and this paper does not try to be conclusive by any means.