Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition Method-Reference-Cited by-同舟云学术

Detecting and Diagnosing Process Nonlinearity- Induced Unit-Wide Oscillations Based on an Optimized Multivariate Variational Mode Decomposition Method

Published:2022 Issue: Volume:10 Page:36106-36122
ISSN:2169-3536
Container-title:IEEE Access
language:
Short-container-title:IEEE Access

Author:

Lin Zhuliang¹,Sun Min²,Wu Xialai³^ORCID

Affiliation:

1. Xingzhi College, Zhejiang Normal University, Lanxi, China

2. Zhejiang Kende Mechanical and Electrical Company Ltd., Taizhou, China

3. School of Engineering, Huzhou University, Huzhou, China

Funder

Grant from the Major Project of Science and Technology Plan of Jinhua, China

Natural Science Foundation of Huzhou, China

Publisher

Institute of Electrical and Electronics Engineers (IEEE)

Subject

General Engineering,General Materials Science,General Computer Science,Electrical and Electronic Engineering

Link

http://xplorestaging.ieee.org/ielx7/6287639/9668973/09745576.pdf?arnumber=9745576

Reference41 articles.

1. Detection and root cause analysis of multiple plant-wide oscillations using multivariate nonlinear chirp mode decomposition and multivariate Granger causality

2. Multivariate nonlinear chirp mode decomposition

3. Self-tuning variational mode decomposition

4. Adaptive Clutter Filtering for Ultrafast Doppler Imaging of Blood Flow Using Fast Multivariate Empirical Mode Decomposition

5. Analysis of Modulated Multivariate Oscillations

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