A prediction method of operation trend for large axial-flow fan based on vibration-electric information fusion

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.

Keywords

large axial-flow fan / early fault / state prediction / particle swarm optimization

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