Abstract

Data streams are prevalent in current manufacturing and service systems where real-time data arrive progressively. A quick distribution inference from such data streams at their early stages is extremely useful for prompt decision making in many industrial applications. For example, a quality monitoring scheme can be quickly started if the process data distribution is available and the optimal inventory level can be determined early once the customer demand distribution is estimated. To this end, this article proposes a novel online recursive distribution inference method for stationary data streams that can respond as soon as the streaming data are generated and update as regularly as the data accumulate. A major challenge is that the data size might be too small to produce an accurate estimation at the early stage of data streams. To solve this, we resort to an instance-based transfer learning approach which integrates a sufficient amount of auxiliary data from similar processes or products to aid the distribution inference in our target task. Particularly, the auxiliary data are reweighted automatically by a density ratio fitting model with a prior-belief-guided regularization term to alleviate data scarcity. Our proposed distribution inference method also possesses an efficient online algorithm with recursive formulas to update upon every incoming data point. Extensive numerical simulations and real case studies verify the advantages of the proposed method.

摘要

数据流在实时数据逐渐到达的当前制造和服务系统中很普遍。在早期阶段从此类数据流中进行快速分布推断对于许多工业应用中的快速决策非常有用。例如，如果过程数据分布可用，则可以快速启动质量监控计划，并且一旦估计了客户需求分布，就可以及早确定最佳库存水平。为此，本文提出了一种新颖的静态数据流在线递归分布推理方法，该方法可以在流数据生成后立即响应，并随着数据的积累而定期更新。一个主要的挑战是数据大小可能太小而无法在数据流的早期阶段产生准确的估计。为了解决这个问题，我们采用基于实例的迁移学习方法，该方法集成了来自类似过程或产品的足够数量的辅助数据，以帮助我们目标任务中的分布推理。特别是，辅助数据通过具有先验信念引导的正则化项的密度比拟合模型自动重新加权，以缓解数据稀缺性。我们提出的分布推理方法还具有高效的在线算法，该算法具有递归公式以更新每个传入的数据点。大量的数值模拟和实际案例研究验证了所提出方法的优点。辅助数据通过具有先验信念引导的正则化项的密度比拟合模型自动重新加权，以缓解数据稀缺性。我们提出的分布推理方法还具有高效的在线算法，该算法具有递归公式以更新每个传入的数据点。大量的数值模拟和实际案例研究验证了所提出方法的优点。辅助数据通过具有先验信念引导的正则化项的密度比拟合模型自动重新加权，以缓解数据稀缺性。我们提出的分布推理方法还具有高效的在线算法，该算法具有递归公式以更新每个传入的数据点。大量的数值模拟和实际案例研究验证了所提出方法的优点。