基于多重分形与SVM的高速列车运行状态识别方法

为了评估高速列车服役性态问题,提出基于多重分形与支持向量机(SVM)的高速列车状态识别新方法.该方法计算了高速列车振动信号的多重分形谱,分析了多重分形谱参数与列车状态之间的关联关系,提取了多重分形谱宽度、分形维数差和谱偏斜度作为高速列车状态的特征,使用支持向量机来对高速列车状态进行识别.获取了某型列车的正常状态、抗蛇行减震器失效、空簧失效3种典型的多重分形特征,训练了不同速度下的SVM和单一速度为160 km/h的SVM,并进行了工况识别实验.所提方法对高速列车的状态识别率大于88.8%,表明了该方法的有效性. 

Recognizing Running State of High-Speed Trains Based on Multifractal Theory and SVM

In order to evaluate in-service performances of high-speed trains, a novel approach to recognize the running state of high-speed trains was proposed using the multifractal theory and the support vector machine (SVM). The relationship between the multifractal spectrum parameters and the train running states was analyzed after the multifractal spectrum of the vibration signal was calculated by multifractal theory. Then, high-speed train running states were identified by SVM, using the characteristics of the multifractal spectrum width, the fractal dimension difference, and the spectrum skewness. In addition, a recognition experiment was carried out for three typical conditions of a certain type train, including the normal condition, the anti-hunting damper malfunction, and the air spring damper malfunction, after the SVM with different velocities and the SVM with a velocity (160 km/h) were trained using their multifratal characteristics. As a result, a state recognition accuracy of more than 88.8% was obtained, which verified the effectiveness of the proposed method.