变精度粗糙集在齿轮故障诊断中的应用

Fault Diagnosis of Bearing Based on Order-Wavelet Packet and Rough Sets

在二级齿轮箱的变负载过程中,为了有效地处理非平稳信号,采用小波包提取特征参量(条件属性值);为了有效地处理带噪声的数据,将变精度粗糙集理论引入到齿轮的故障诊断中,提出了一种条件属性约简方法。首先对连续属性进行离散化;然后定义集合M,根据实际情况,选取不同的正确分类率β,利用变精度粗糙集的近似分类质量进行条件属性约简,并与加入噪声数据后所得的约简结果进行了对比;最后通过齿轮故障实例验证了此方法的有效性和实用性。

In the load change test,the vibration signals are measured on a two-level gearbox testbed.For coping with non-stationary signals effective,wavelet packet is used for obtaining a feature vector(values of condition attribute).For coping with noise data effective,variable precision rough set(VPRS) theory is introduced in the fault diagnosis of the gear,and a kind method of condition attribute reduction is presented.Firstly,continuous attributes are discretized. Then,the set of M is defined,and varying values of β(right ratio of classification) are selected according to the reality,and the quality of approximation classification of VPRS is utilized to carry through condition attribute reduction and contrasted its result with the reduction result coming from data(including noise data).Finally,a practical example in the gear shows the validity and practicability of the algorithm.