基于WCFSE-FSVM的转子振动故障诊断方法

Rotor Vibration Fault Diagnosis Method Based on WCFSE-FSVM

为了提高含有噪声和野值的转子振动故障样本诊断精度,提出了基于WCFSE-FSVM的故障诊断方法。充分融合小波相关特征尺度熵(WCFSE)特征提取方法和FSVM故障诊断方法的优点,建立WCFSE-FSVM故障诊断模型。基于转子实验台模拟4种典型故障,获得原始故障数据;并利用WCFSE方法提取这些故障数据的WCFSE值,选取故障信号高频段中的尺度1和尺度2上的小波相关特征尺度熵W1和W2构造出振动信号的故障向量作为故障样本,建立FSVM诊断模型。实例分析显示:WCFSE-FSVM方法的转子故障诊断精度最高,即故障类别诊断精度为94.49%,故障严重程度的诊断精度为95.58%,二者都优于其它故障诊断方法。验证了WCFSEFSVM方法的可行性和有效性。

To improve the diagnostic precision of rotor vibration fault samples with the noise and outli- ers, the fault diagnosis method based on the wavelet correlation feature scale entropy （WCFSE） and fuzzy support vector machine （FSVM） （WCFSE-FSVM） was proposed. The fault diagnosis model of the WCFSE- FSVM was established by fully fusing the strength of the WCFSE feature extraction method and the FSVM fault diagnosis method. The original fault data were gained through simulating four typical faults on rotor test-bed. The WCFSE values of these data were extracted by the WCFSE method, and W1 and W2 , which are the WCFSE values on the scales 1 and 2, respectively, in the high band of fault signals, were selected to construct the fault vectors of vibration signals as the fault samples for establishing the FSVM diagnosis mod- el. As shown by instance analysis, the WCFSE-FSVM in four methods possesses the highest diagnosis preci- sion that the fault type and severity diagnosis precisions of rotor vibration are 94.49% and 95.58% , re- spectively. This paper demonstrates the validity and feasibility of proposed WCFSE-FSVM and provides an effective method for rotor vibration fault diagnosis.