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Condition-based Maintenance for Performance Degradation under Non-periodic Unreliable Inspections

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Résumé

With the development of monitoring technology, increasing attention has been paid to condition-based maintenance (CBM). Also, if condition information for maintenance can be obtained with fewer inspections, the cost of the entire maintenance process could be reduced. However, in application, the monitoring equipment cannot maintain always a reliable operating state, for example, due to various uncertain factors, such as sensor errors, component tolerances, and environment disturbances, the inspections from the sensors are often unreliable. Motivated by these simple observations, we propose a non-periodic condition-based maintenance policy under unreliable inspections. The time interval of the non-periodic inspections is obtained via an inspection scheduling function. The unknown parameters of the component degradation process are updated by gradient descent, while simultaneously the maintenance decision variables are adjusted. A catastrophe strategy-based particle swarm optimization is used to set the optimal decision variables by minimizing the long-run cost rate. Application to laser degradation data illustrates the effectiveness of the proposed method.
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Dates et versions

hal-03908082 , version 1 (20-12-2022)

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Citer

Yang Li, Yan Shi, Zhiyao Zhang, Ningyun Lu, Xiuli Wang, et al.. Condition-based Maintenance for Performance Degradation under Non-periodic Unreliable Inspections. IEEE Transactions on Artificial Intelligence, 2022, pp.1-13. ⟨10.1109/TAI.2022.3197680⟩. ⟨hal-03908082⟩
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