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arXiv · 1812.10430

Large Multistream Data Analytics for Monitoring and Diagnostics in Manufacturing Systems

Samaneh Ebrahimi·Chitta Ranjan·Kamran Paynabar
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Abstract

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.

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Samaneh Ebrahimi, Chitta Ranjan, Kamran Paynabar. 2018-12-26. Large Multistream Data Analytics for Monitoring and Diagnostics in Manufacturing Systems. https://arxiv.org/abs/1812.10430

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