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A real time methodology of cluster-system theory-based reliability estimation using k-means clustering

机译：使用K-means群集的基于集群系统理论的可靠性估计的实时方法

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摘要

With rapidly increase in the design and application of complex system engineering, life and reliability analysis methods for these engineering have received much attention. A novel real time reliability analysis methodology is proposed based on the cluster-system theory and k-means clustering. Firstly, the system consisting of three or more identical or similar sub-systems facing the same load or task is defined as cluster-system. By analyzing the key performance parameters, sub-systems with similar performance are divided into a family-system, so that each sub-system can be used as reference sample for other sub-systems in the identical family-system. The cubic spline interpolation method under first-order boundary conditions is used to fit the average variation of key performance parameters of family-systems. The residual life of family-system can be obtained by determining the threshold of key performance parameters of sub-system through experiments or experience. Then reliability of the whole system is estimated by the contribution of each sub-system in cluster-system to solve the problem that there is no fault data and reference samples in the reliability analysis of complex system. Application in the Five-hundred-meter Aperture Spherical Telescope (FAST) demonstrates the effectiveness of the proposed method, compared to traditional reliability analysis methods based on experimental statistics.
机译：随着复杂系统工程的设计和应用迅速增加，这些工程的生活和可靠性分析方法受到了很多关注。基于群集系统理论和K-Means聚类，提出了一种新颖的实时可靠性分析方法。首先，由面向相同的负载或任务的三个或更多个相同或类似的子系统组成的系统被定义为群集系统。通过分析关键性能参数，具有类似性能的子系统被分成家庭系统，因此每个子系统可以用作相同家庭系统中的其他子系统的参考样本。在一阶边界条件下的立方样条插值方法用于符合家庭系统的关键性能参数的平均变化。通过确定通过实验或经验来确定子系统的关键性能参数的阈值，可以获得家庭系统的剩余寿命。然后通过集群系统中的每个子系统的贡献来解决整个系统的可靠性来解决在复杂系统的可靠性分析中没有故障数据和参考样本的问题。在五百米孔径球形望远镜（FAST）中的应用展示了该方法的有效性，与基于实验统计的传统可靠性分析方法相比。

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