论文标题
使用神经网络的多重复组件系统的检查计划预测
Inspection plan prediction for multi-repairable component systems using neural network
论文作者
论文摘要
实施适当的维护政策将有助于我们拥有更可靠的系统并降低总成本。在本文中，为可维修的多组分系统提出了动态维护计划，其中每个组件都会受到两个竞争性降解和随机冲击的失败过程。对于具有单独维修组件的系统，如果失败，则替换整个系统是不经济的。在任何检查时，都可以检测到失败的组件并替换为新的组件，而其他组件则继续运行。因此，在任何检查时间的每个组件的初始年龄都与其他组件不同。不同的初始年龄会影响应检查整个系统的最佳时间。考虑到系统中所有组件的初始年龄及其配置，应动态地计算最佳检查时间。在本文中，使用神经网络方法来预测检查开始时组件初始年龄的系统的下一个最佳检查时间。系统的可靠性和成本率功能被制定并用于训练预测模型。拟议的维护计划由数值示例证明
Implementing an appropriate maintenance policy would help us to have a more reliable system and reduce the total costs. In this paper, a dynamic maintenance plan is proposed for repairable multi-component systems, where each component is subject to two competing failure processes of degradation and random shock. For systems with individually repairable components, it is not economical to replace the whole system if it fails. At any inspection time, the failed components can be detected and replaced with a new one and the other components continue functioning; therefore, the initial age of each component at any inspection time is different from other components. Different initial ages have an effect on the optimal time that the whole system should be inspected. The optimal inspection time should be calculated dynamically considering the initial age of all the components and their configuration within the system. In this paper, a neural network method is used to predict the next optimal inspection time for systems considering the initial age of components at the beginning of the inspection. System reliability and cost rate function are formulated and used to train the prediction model. The proposed maintenance plan is demonstrated by numerical examples
