Please use this identifier to cite or link to this item: https://hdl.handle.net/10356/99359
Title: A framework to practical predictive maintenance modeling for multi-state systems
Authors: Tan, Cher Ming
Raghavan, Nagarajan
Keywords: DRNTU::Engineering::Systems engineering
Issue Date: 2007
Source: Tan, C. M., & Raghavan, N. (2008). A framework to practical predictive maintenance modeling for multi-state systems. Reliability Engineering & System Safety, 93(8), 1138-1150.
Series/Report no.: Reliability engineering & system safety
Abstract: A simple practical framework for predictive maintenance (PdM)-based scheduling of multi-state systems (MSS) is developed. The maintenance schedules are derived from a system-perspective using the failure times of the overall system as estimated from its performance degradation trends. The system analyzed in this work is a flow transmission water pipe system. The various factors influencing PdM-based scheduling are identified and their impact on the system reliability and performance are quantitatively studied. The estimated times to replacement of the MSS may also be derived from the developed model.The results of the model simulation demonstrate the significant impact of maintenance quality and the criteria for the call for maintenance (user demand) on the system reliability and mean performance characteristics. A slight improvement in maintenance quality is found to 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. The studies also reveal that in order to reduce the frequency of maintenance actions, it is necessary to lower the minimum user demand from the system if possible, 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 industrial systems’ needs.
URI: https://hdl.handle.net/10356/99359
http://hdl.handle.net/10220/17634
ISSN: 0951-8320
DOI: 10.1016/j.ress.2007.09.003
Rights: © 2007 Elsevier Ltd.
Fulltext Permission: none
Fulltext Availability: No Fulltext
Appears in Collections:EEE Journal Articles

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