Abstract

The high-dimensionality and volume of large-scale streaming data has inhibited significant research progress in developing an integrated monitoring and diagnostics (M&D) approach. Such data streams are becoming common in various applications including manufacturing, healthcare, and web mining. In this article, we propose an integrated M&D approach for large-scale streaming data. Using principal component analysis (PCA), we first develop a new monitoring method that adaptively chooses principal components that are most likely to be affected by the process change. Furthermore, we propose a novel diagnostic approach, seamlessly integrated with the proposed monitoring method to enable a streamlined SPC. This diagnostics approach draws inspiration from compressed sensing and uses adaptive lasso for identifying the sparse sources of the process change. We theoretically motivate our method and evaluate our integrated M&D method through simulations and case studies.

摘要

大规模流数据的高维和海量阻碍了开发集成的研究进展。监测和诊断 (M&D) 方法。此类数据流在各种应用中变得越来越普遍，包括制造、医疗保健和网络挖掘。在本文中，我们提出了一种针对大规模流数据的集成 M&D 方法。使用主成分分析 (PCA)，我们首先开发了一种新的监控方法，可以自适应地选择最有可能受过程变化影响的主成分。此外，我们提出了一种新颖的诊断方法，与所提出的监测方法无缝集成，以实现简化的 SPC。这种诊断方法从压缩感知中汲取灵感，并使用自适应套索来识别过程变化的稀疏来源。我们从理论上激励我们的方法并通过模拟和案例研究评估我们的综合 M&D 方法。