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 Tools annual

Global: Zendy Free Plan

Global: Zendy Tools monthly

Global: Zendy Plus monthly

By integrating two powerful methods of density reduction and intrinsicdimensionality estimation, a new data-driven method, referred to as OLPP-MLE(orthogonal locality preserving projection-maximum likelihood estimation), isintroduced for process monitoring. OLPP is utilized for dimensionalityreduction, which provides better locality preserving power than localitypreserving projection. Then, the MLE is adopted to estimate intrinsicdimensionality of OLPP. Within the proposed OLPP-MLE, two new static measuresfor fault detection $T_{\scriptscriptstyle {OLPP}}^2$ and ${\rmSPE}_{\scriptscriptstyle {OLPP}}$ are defined. In order to reduce algorithmcomplexity and ignore data distribution, kernel density estimation is employedto compute thresholds for fault diagnosis. The effectiveness of the proposedmethod is demonstrated by three case studies.

Process monitoring based on orthogonal locality preserving projection with maximum likelihood estimation