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Condition-based maintenance optimization for multi-component systems subject to a system reliability requirement

机译：基于条件的维护优化，对系统可靠性要求进行多分量系统

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摘要

Prognostic methods for remaining useful life and reliability prediction have been extensively studied in the past decade. However, the use of prognostic information and methods in maintenance decision-making for complex systems is still an underexplored area. In this paper, using a rolling-horizon approach, we develop a condition-based maintenance decision-framework for a multi-component system subject to a system reliability requirement. The system is inspected periodically and new degradation information on components is obtained upon inspection. The new degradation observations are used to update the posterior distributions of the failure model parameters via Bayesian updating, providing more accurate and customized predictive reliabilities. If the predictive system reliability is below the reliability requirement, a novel dynamic-priority-based heuristic algorithm is used to identify a group of components for preventive maintenance. Numerical results show that significant cost savings and improved system reliabilities can be obtained by using more accurate predictive information in maintenance decision-making. We further illustrate the modeling flexibility of the proposed framework by considering dynamic environmental information in decision-making and investigate the potential benefits of incorporating dynamic contexts.
机译：过去十年来说，对剩余使用寿命和可靠性预测的预后方法已被广泛研究。然而，用于复杂系统的维护决策中的预后信息和方法仍然是一个偏远的地区。本文采用了滚动地平线方法，我们开发了一种基于条件的维护决策框架，用于多组件系统，该系统可靠性要求。在检查时，定期检查系统，并在检查时获得有关组件的新劣化信息。新的降解观察用于通过贝叶斯更新更新失败模型参数的后部分布，提供更准确和定制的预测可靠性。如果预测系统可靠性低于可靠性要求，则使用一种基于新的动态优先级的启发式算法来识别预防性维护的一组组件。数值结果表明，通过在维护决策中使用更准确的预测信息，可以获得显着的成本节约和改进的系统可靠性。我们进一步通过考虑决策中的动态环境信息来进一步说明所提出的框架的建模灵活性，并调查纳入动态背景的潜在益处。

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