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Imperfect predictive maintenance model for multi-state systems with multiple failure modes and element failure dependency

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Imperfect predictive maintenance model for multi-state systems with multiple failure modes and element failure dependency

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dc.contributor.author Tan, Cher Ming
dc.contributor.author Raghavan, Nagarajan
dc.date.accessioned 2010-08-23T07:16:42Z
dc.date.available 2010-08-23T07:16:42Z
dc.date.copyright 2010
dc.date.issued 2010-08-23T07:16:42Z
dc.identifier.citation Tan, C. M., & Raghavan, N. (2010). Imperfect predictive maintenance model for multi-state systems with multiple failure modes and element failure dependency. Prognostics and System Health Management Conference (pp. 1-12) Macau.
dc.identifier.uri http://hdl.handle.net/10220/6347
dc.description.abstract The objective of this study is to develop a practical statistical model for imperfect predictive maintenance based scheduling of multi-state systems (MSS) with reliability dependent elements and multiple failure modes. The system is modeled using a Markov state diagram and reliability analysis is performed using the Universal Generating Function (UGF) technique. The model is simulated for a case study of a power generation transmission system. The various factors influencing the predictive maintenance (PdM) policy such as maintenance quality and user threshold demand are examined and the impact of the variation of these factors on system performance is quantitatively studied. The model is found to be useful in determining downtime schedules and estimating times to replacement of an MSS under the PdM policy. The maintenance schedules are devised based on a "system-perspective" where failure times are estimated by analyzing the overall performance distribution of the system. Simulation results of the model reveal that a slight improvement in the "maintenance quality" can postpone the system replacement time by manifold. The consistency in the quality of maintenance work with minimal variance is also identified as a very important factor that enhances the system's future operational and downtime event predictability. Moreover, the studies reveal that in order to reduce the frequency of maintenance actions, it is necessary to lower the minimum user expectations from the system, ensuring at the same time that the system still performs its intended function effectively. The model proposed can be utilized to implement a PdM program in the industry with a few modifications to suit the individual industry's needs.
dc.format.extent 12 p.
dc.language.iso en
dc.rights © 2010 IEEE. Personal use of this material is permitted. However, permission to reprint/republish this material for advertising or promotional purposes or for creating new collective works for resale or redistribution to servers or lists, or to reuse any copyrighted component of this work in other works must be obtained from the IEEE. This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder. http://www.ieee.org/portal/site This material is presented to ensure timely dissemination of scholarly and technical work. Copyright and all rights therein are retained by authors or by other copyright holders. All persons copying this information are expected to adhere to the terms and constraints invoked by each author's copyright. In most cases, these works may not be reposted without the explicit permission of the copyright holder.
dc.subject DRNTU::Engineering::Electrical and electronic engineering::Control and instrumentation::Control engineering.
dc.subject DRNTU::Engineering::Electrical and electronic engineering::Microelectronics.
dc.title Imperfect predictive maintenance model for multi-state systems with multiple failure modes and element failure dependency
dc.type Conference Paper
dc.contributor.conference Prognostics and System Health Management Conference (2010 : Macau, China)
dc.contributor.research Singapore Institute of Manufacturing Technology
dc.contributor.school School of Electrical and Electronic Engineering
dc.identifier.doi http://dx.doi.org/10.1109/PHM.2010.5414594
dc.description.version Published version

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