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Statistically Planned and Individually Improved Predictive Maintenance Management for Continuously Monitored Degrading Systems

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4 Author(s)
Ming-Yi You ; State Key Lab. of Mech. Syst. & Vibration, Shanghai Jiao Tong Univ., Shanghai, China ; Fang Liu ; Wen Wang ; Guang Meng

This paper proposes a statistically planned and individually improved predictive maintenance (SPII PdM) policy for a batch of single-unit systems. The SPII PdM policy simultaneously takes advantage of 1) the capability of classical statistical lifetime distribution based (SLD-based) preventive maintenance (PM) policies for long-term planning, and 2) the capability of PdM techniques for predicting the residual life (RL) of an individual system. Within the framework of a classical SLD-based PM policy aiming at maximizing long-term average availability, two lifetime margins are proposed in the decision making process to further improve the availability of some (but not all) individuals. A numerical experiment based on a typical degradation model is used to compare the performance of the proposed policy with that of the classical SLD-based PM policy. The comparison results show that higher average availability is achieved in the SPII PdM policy with a decent RL prediction model. For practical implementation, the current study demonstrates the possibility of partially applying the emerging PdM techniques in the widely used SLD-based PM policy in a (approximately) theoretically effective manner.

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Reliability, IEEE Transactions on  (Volume:59 ,  Issue: 4 )