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.

作为关键设备，大型轴流风机（LAF）广泛应用于公路隧道的通风。请注意，LAF 的任何故障都会对交通造成严重后果。具体而言，当在早期故障阶段检测到异常状态时，极大地抑制了故障恶化。因此，由于信号幅度和系统干扰（或噪声）低，早期故障特征的监测非常困难。为了克服这个问题，本文提出了一种新的故障早期判断方法来预测运行趋势。提出并讨论了振动-电信息融合、具有粒子群优化 (PSO) 的支持向量机 (SVM) 和用于预测 LAF 操作状态的交叉验证 (CV)。最后，