基于LCD和排列熵的滚动轴承故障诊断

A Rolling Bearing Fault Diagnosis Method Based on LCD and Permutation Entropy

排列熵(permutation entropy,简称PE)是最近提出的一种检测时间序列随机性和动力学突变行为的方法,可以考虑将其应用于故障诊断。由于机械系统的复杂性,振动信号的随机性和动力学突变行为表现在不同尺度上,因此需要对振动信号进行多尺度的排列熵分析。基于此,提出了基于局部特征尺度分解(local characteristicscale decomposition,简称LCD)和排列熵的滚动轴承故障诊断方法。首先,采用LCD方法对振动信号进行自适应分解,得到不同尺度的的本征尺度分量(intrinsic scale component,简称ISC);其次,计算前几个包含主要故障信息的ISC分量的排列熵;最后,将熵值作为特征向量,输入基于神经网络集成建立的分类器。将该方法应用于滚动轴承实验数据,分析结果表明,此方法可有效实现滚动轴承的故障诊断。

Permutation entropy(PE)is a new method proposed for detecting the randomicity and dynamic changes of time series,which can be used in the field of fault diagnosis.However,due to the complexity of mechanical systems,the randomicity and dynamic changes of the vibration signal behave on different scales,making it necessary to analyze the vibration signal with permutation entropy in a multi-scale way.Therefore,a new method of rolling bearing fault diagnosis based on PE and the local characteristic-scale decomposition(LCD)is put forward.Firstly,the LCD method is used to decompose the vibration signal,and ISCs spanning different scales are obtained.Secondly,the permutation entropy of the first few ISC components,which contain the main fault information,is calculated.The entropies are accordingly seen as the characteristic vector,then input to the neural network ensemble based classifier.Finally,the proposed method is applied to the experimental data.The analysis results show that the proposed approach can effectively achieve fault diagnosis of rolling bearings.