Large Multistream Data Analytics for Monitoring and Diagnostics in Manufacturing Systems
This addresses monitoring and diagnostics for manufacturing plants dealing with big data, though it appears incremental as it builds on existing methods like principal component analysis and compressed sensing.
The paper tackles the challenge of monitoring and diagnostics in manufacturing systems with high-dimensional, large-scale multistream data by proposing an integrated approach using Adaptive Principal Component monitoring for early detection and Principal Component Signal Recovery for diagnostics, achieving improved performance in simulations and case studies.
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.