An Integrated Approach to Oscillation Propagation Identification and Source Locating in Process Multi-loop Systems

曹稼斌; 张亮; 郑建荣; 夏春明

doi:10.1016/S1004-9541(11)60083-9

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An Integrated Approach to Oscillation Propagation Identification and Source Locating in Process Multi-loop Systems

Updated：2026-01-06

- An Integrated Approach to Oscillation Propagation Identification and Source Locating in Process Multi-loop Systems
- Chinese Journal of Chemical Engineering Vol. 19, Issue 6, Pages: 999-1008(2011)
- Affiliations：
  
  Department of Mechanical Engineering, East China University of Science and Technology,Shanghai,China,200237
- Author bio：
- Funds：
- DOI：10.1016/S1004-9541(11)60083-9
  CLC：
- Published：2011
- Accepted：
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CAO Jiabin, ZHANG Liang, ZHENG Jianrong, XIA Chunming. An Integrated Approach to Oscillation Propagation Identification and Source Locating in Process Multi-loop Systems[J]. , 2011, 19(6): 999-1008.
DOI：

CAO Jiabin, ZHANG Liang, ZHENG Jianrong, XIA Chunming. An Integrated Approach to Oscillation Propagation Identification and Source Locating in Process Multi-loop Systems[J]. , 2011, 19(6): 999-1008. DOI： 10.1016/S1004-9541(11)60083-9.

摘要

Abstract

An integrated method for identifying the propagation of multi-loop process oscillations and their source location is proposed in this paper. Oscillatory process loop variables are automatically selected based on the component-related ratio index and a mixing matrix

both of which are obtained in data preprocessing by spectral independent component analysis. The complex causality among oscillatory process variables is then revealed by Granger causality test and is visualized in the form of causality diagram. The simplification of causal connectivity in the diagram is performed according to the understanding of process knowledge and the final simplest causality diagram

which represents the main oscillation propagation paths

is achieved by the automated cutting-off thresh-old search

with which less significant causality pathways are filtered out. The source of the oscillation disturbance can be identified intuitively through the final causality diagram. Both simulated and real plant data tests are presented to demonstrate the effectiveness and feasibility of the proposed method.

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