二叉树支持向量机的旋转机械故障诊断

针对SVM二叉树多类分类优先级的确定问题,通过旋转机械故障实验平台和数据采集系统,采集旋转机械故障实验台转子正常、转子不平衡、转子不对中、转子轴承内圈裂缝、转子轴承外圈裂缝5种工况下的振动信号,进行零均值化处理;选择信号的主要频段进行信号重组,提取其时域无量纲特征值,利用并联式SVM的正检率大小确定SVM二叉树多类分类的优先级,进行故障类型的识别。通过实验,实现了训练样本的完全可分,说明此种方法的有效性。

Fault diagnosis of rotating machinery based on SVM 2PTMC

A method is proposed to determine the priority class of binary tree SVM. Firstly, the vibration signals of rotating machinery are colleted through a rotating machinery fault experimental platform and data acquisition system. The signals are from 5 different conditions, i.e. rotor normal, rotor unbalance, rotor misalignment, rotor bearing inner ring cracks and rotor bearing outer ring cracks. Then the signals are disposed by zero-mean and the main frequency band of the vibration signals are reconstructed to, extract the dimensionless time domain as characteristic value. Finally, the priority class of SVM 2PTMC can be determined by the correct inspection rate of parallel SVM. Training samples can be completely divided in experiments, which verifies the effectiveness of this method.