  The high-dimensionality and volume of large scale multistream data has
inhibited significant research progress in developing an integrated monitoring
and diagnostics (M&D) approach. This data, also categorized as big data, is
becoming common in manufacturing plants. In this paper, we propose an
integrated M\&D approach for large scale streaming data. We developed a novel
monitoring method named Adaptive Principal Component monitoring (APC) which
adaptively chooses PCs that are most likely to vary due to the change for early
detection. Importantly, we integrate a novel diagnostic approach, Principal
Component Signal Recovery (PCSR), to enable a streamlined SPC. This diagnostics
approach draws inspiration from Compressed Sensing and uses Adaptive Lasso for
identifying the sparse change in the process. We theoretically motivate our
approaches and do a performance evaluation of our integrated M&D method through
simulations and case studies.
