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中南大学学报(英文版)

Journal of Central South University

Vol. 28    No. 6    June 2021

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A prediction method of operation trend for large axial-flow fan based on vibration-electric information fusion
GU Zhen-yu(谷振宇), ZHU Yao-yao(朱垚垚), XIANG Ji-lei(向继磊), ZENG Yuan(曾圆)

College of Automation, Chongqing University, Chongqing 400044, China

Abstract:As the critical equipment, large axial-flow fan (LAF) is used widely in highway tunnels for ventilating. Note that any malfunction of LAF can cause severe consequences for traffic. Specifically, fault deterioration is suppressed tremendously when an abnormal state is detected in the stage of early fault. Thus, the monitoring of the early fault characteristics is very difficult because of the low signal amplitude and system disturbance (or noise). In order to overcome this problem, a novel early fault judgment method to predict the operation trend is proposed in this paper. The vibration-electric information fusion, the support vector machine (SVM) with particle swarm optimization (PSO), and the cross-validation (CV) for predicting LAF operation states are proposed and discussed. Finally, the results of the experimental study verify that the performance of the proposed method is superior to that of the contrast models.

 

Key words: large axial-flow fan; early fault; state prediction; particle swarm optimization

中南大学学报(自然科学版)
  ISSN 1672-7207
CN 43-1426/N
ZDXZAC
中南大学学报(英文版)
  ISSN 2095-2899
CN 43-1516/TB
JCSTFT
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