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.