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

Dynamic principal component analysis (DPCA) is generally employed in monitoring dynamic processes and typically incorporates all measured variables. However, for a large-scale process, the inclusion of variables without fault-relevant information may cause redundancy and degrade monitoring performance. In this paper, the influence of variable and time-lagged variable selection on the DPCA monitoring performance is analyzed. Then, a fault-relevant performance-driven distributed monitoring scheme is proposed to achieve efficient fault detection and diagnosis. First, performance-driven process decomposition is performed, and the optimal subset of variables and time-lagged variables for each fault are selected through a stochastic optimization algorithm. Second, local DPCA models are established to characterize the process dynamics and generate fault signature evidence. Finally, a Bayesian diagnosis system with the most efficient evidence sources is established to identify the process status. Case studies on a numerical example and the Tennessee Eastman benchmark process demonstrate the efficiency of the proposed monitoring scheme.

Data-Driven Optimized Distributed Dynamic PCA for Efficient Monitoring of Large-Scale Dynamic Processes