基于振动-电参量信息融合的大型轴流风机运行趋势预测方法

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

大型轴流式通风机作为公路隧道通风的关键设备,得到了广泛的应用。轴流风机产生的任何故障都可能对交通造成严重后果。如果在早期故障阶段能检测到异常状态,就可以极大地抑制故障劣化。针对大型轴流风机故障初期表征不明显,以及由风机外壳对振动信号的滤波作用,导致的所测信号对内部故障不敏感,在线智能故障诊断及时性差、灵敏度低等问题,提出一种基于振动信号-功率信息融合的大型轴流风机趋势预测方法。将提取的特征向量作为粒子群和交叉验证优化的支持向量机的输入实现轴流风机运行趋势的预测。实验结果表明,该方法的性能优于基准方法。

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.