English (United Kingdom)

https://curated-unify.zendy.io/wp-json/zendy-region/v1/featured_content/oa?rat=en

https://curated-unify.zendy.io/wp-json/zendy-region/v1/highlighted_journal/

Global: Zendy Plus annual

Presents the access of premium content as premium feature

Premium Content

Presents the keyphrase highlighting as premium feature

Keyphrase Highlighting

Presents the summarisation as premium feature

Summarisation

Insights

Presents the pdf analysis as premium feature

PDF Analysis

Presents the zaia usage as premium feature

ZAIA

Global: Zendy Plus monthly

Global: Zendy Tools annual

Global: Zendy Tools monthly

Global: Zendy Free Plan

The high-dimensionality and volume of large scale multistream data hasinhibited significant research progress in developing an integrated monitoringand diagnostics (M&D) approach. This data, also categorized as big data, isbecoming common in manufacturing plants. In this paper, we propose anintegrated M\&D approach for large scale streaming data. We developed a novelmonitoring method named Adaptive Principal Component monitoring (APC) whichadaptively chooses PCs that are most likely to vary due to the change for earlydetection. Importantly, we integrate a novel diagnostic approach, PrincipalComponent Signal Recovery (PCSR), to enable a streamlined SPC. This diagnosticsapproach draws inspiration from Compressed Sensing and uses Adaptive Lasso foridentifying the sparse change in the process. We theoretically motivate ourapproaches and do a performance evaluation of our integrated M&D method throughsimulations and case studies.

Large Multistream Data Analytics for Monitoring and Diagnostics in Manufacturing Systems