基于OC-VPMCD和ITD的滚动轴承故障诊断方法

基于变量预测模型的模式识别方法可以充分利用从原始数据中所提取的特征值之间的相互内在关系建立数学模型并以预测误差平方和值最小为判别函数进行分类。基于此,提出了一种新的一类分类方法--单类基于变量预测模型的模式识别（OC-VPMCD）方法,将该方法与本征时间尺度分解（ITD）方法相结合并应用于滚动轴承故障诊断。首先采用ITD对滚动轴承振动信号进行分解并对包含主要故障信息的若干固有旋转（PR）分量提取排列熵作为故障特征值;然后对OC-VPMCD分类器进行训练,并确定预测误差平方和阈值;最后进行OC-VPMCD模式识别,根据模式识别结果判断滚动轴承的工作状态正常与否。实验数据分析结果表明,该方法能够有效地应用于滚动轴承振动信号的故障诊断。

Rolling Bearing Fault Diagnosis Method Based on OC-VPMCD and ITD

Variable predictive model based class discriminate (VPMCD) is a way to pattern recognitions. It made full use of the inner relations among characteristic values extracted from those original data to recognize models and classified the faults by minimum prediction error sum of squares value. Based on that, the paper proposed a new one-class classification method-OC-VPMCD and combined OC-VPMCD with ITD and applied into the rolling bear fault diagnosis. Firstly, rolling bearing vibration signals would be adaptively decomposed by ITD and the permutation entropy of proper rotations (PR) which contain the main fault information would be extracted as characteristic values. Secondly, OC-VPMCD classifier would be trained and determined the prediction error sum of squares threshold value. Finally, the OC-VPMCD classifier would be used to complete pattern recognitions; according to the pattern recognitions results the working states of the rolling bearing were judged. The experimental results show that this method can be applied to rolling bearing fault diagnosis effectively.