多尺度化基本尺度熵及其在故障诊断中的应用

MULTISCALE BASE-SCALE ENTROPY AND ITS APPLICATION IN FAULT DIAGNOSIS

针对机械故障振动信号非线性、非平稳性等特点,提出了一种新的时间序列复杂性度量的方法—多尺度化基本尺度熵(Multiscale base-scale entropy,MBSE)。MBSE是基于基本尺度熵(base-scale entropy,BSE)而定义的,BSE可以对时间序列复杂性和无规则程度进行度量,而MBSE则在BSE的基础上引入了尺度因子,是对时间序列在不同尺度因子下复杂性的量度,包含有更多时间模式信息。介绍了BSE和MBSE的概念,并将其应用于滚动轴承故障振动信号复杂性度量中,由此提出了一种基于MBSE和相关向量机的滚动轴承故障诊断方法。实例分析表明该方法能有效地提取故障特征,实现故障类型的诊断。

Aiming at the fact that the mechanical vibration signal would exactly display non-stationary characteristics,a new method named as multiscale base-scale entropy（ MBSE） is introduced for measuring the complexity of time series,while MBSE which is based on base-scale entropy（ BSE） is defined to measure the complexity and irregularity of time series in different scale factors,and contains more and deeper information of time series. The concepts of BSE and MBSE are introduced firstly in this paper,and then they are used to measure the complexity of rolling bearing fault vibration signals. Finally,a new rolling bearing fault diagnosis method based on multiscale base-scale entropy and relevance vector machine is put forward. The experiment results show that the proposed method can diagnosis different fault state of rolling bearing effectively.