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

Oscillations are a common abnormal phenomenon in the process control system. They can degrade control performance or even cause plant shutdown. It is crucial to accurately detect and characterize the process oscillations. In this article, a novel oscillation detector is proposed by combining the particle swarm optimization (PSO) and nonlinear chirp mode decomposition (NCMD). Because the performance of NCMD relies on the selection of mode number Q and bandwidth parameter &#x03B1;, PSO is utilized to search the optimal parameter pairs. Then, the multiple oscillations contained in the process variables (PV) can be extracted by NCMD with the optimal parameters. The normalized correlation index and sparseness index are used to discarding the spurious modes and quantifying the degree of oscillations, respectively. After detecting, by utilizing the time-frequency information provided by the oscillating modes, multiple oscillation types can be accurately characterized. Comparisons are provided to show the advantages of the proposed method. The effectiveness and utility are validated by simulations as well as various industrial cases.

Detecting and Characterizing Multiple Oscillations in Process Control Loop Based on PSO-NCMD