论文标题
时间序列预测的分层样条：海军发动机故障率的应用
Hierarchical spline for time series forecasting: An application to Naval ship engine failure rate
论文作者
论文摘要
预测设备故障很重要，因为它可以提高可用性并降低运营预算。以前的文献试图通过浴缸形成功能，微博分布，贝叶斯网络或AHP来对失败率进行建模。但是这些模型在足够数量的数据中表现良好，无法纳入两个显着特征。类别和共享结构不平衡。层次模型具有部分合并的优势。所提出的模型基于贝叶斯分层B-Spline。 99大韩民国海军船的故障率的时间序列是层次建模的，每一层都对应于发动机，发动机类型和发动机原型。由于分析，建议的模型预测了整个生命周期的失败率在多种情况条件下，例如对发动机的先验知识。
Predicting equipment failure is important because it could improve availability and cut down the operating budget. Previous literature has attempted to model failure rate with bathtub-formed function, Weibull distribution, Bayesian network, or AHP. But these models perform well with a sufficient amount of data and could not incorporate the two salient characteristics; imbalanced category and sharing structure. Hierarchical model has the advantage of partial pooling. The proposed model is based on Bayesian hierarchical B-spline. Time series of the failure rate of 99 Republic of Korea Naval ships are modeled hierarchically, where each layer corresponds to ship engine, engine type, and engine archetype. As a result of the analysis, the suggested model predicted the failure rate of an entire lifetime accurately in multiple situational conditions, such as prior knowledge of the engine.
