总体局部特征尺度分解的滚动轴承故障诊断

针对滚动轴承非平稳性的振动信号,提出了基于总体局部特征尺度分解(Ensemble Local Characteristic-scale Decomposition, ELCD)的排列熵及相关向量机的滚动轴承故障诊断方法。首先,对振动信号进行ELCD分解,获得一系列内禀尺度分量(Instrinsic Scale Component, ISC);其次,根据分解后ISC分量的峭度值选取主ISC分量,计算主ISC分量的排列熵并将其组合成特征向量;最后,将特征向量输入相关向量机进行训练与测试,从而识别滚动轴承的故障类型。对实验信号的分析表明,该方法能够有效的诊断出滚动轴承不同的工作状态,且效果较局部特征尺度分解方法好。

FAULT DIAGNOSIS OF BALL BEARING BASED ON ELCD PERMUTATION ENTROPY AND RVM

Aiming at the no stationary characteristic of a gear fault vibration signal, it proposes a recognition method based on ELCD(Ensemble local Characteristic-scale decomposition) permutation entropy and RVM. First, the vibration signal was decomposed by ELCD, then a series of intrinsic scale components were obtained;Secondly, according to the kurtosis of ISCs, principal ISCs were selected, then, calculate the permutation entropy of principal ISCs and combined into a feature vector;Finally, the feature vector were input RVM classifier to train and test to identify the type of rolling bearing faults. Experimental results show that this method can effectively diagnosis four kinds of working condition, and the effect is better than local Characteristic-scale decomposition method.