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

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2 Author(s)
Cher Ming Tan ; Division of Circuits & Systems, School of Electrical & Electronic Engineering, Nanyang Technological University, BlockS2, Singapore - 639798 ; Nagarajan Raghavan

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.

Published in:

2010 Prognostics and System Health Management Conference

Date of Conference:

12-14 Jan. 2010