变工况下轴承健康监测的相关向量机与自适应阈值模型方法

工况在旋转机械运行过程中通常是变化的。变化的工况和故障一样,也会引起机械振动特征发生改变,从而引起诊断误差。为此,提出一种用于变工况下轴承健康监测的新方法。该方法使用相关向量机拟合振动特征的统计量随工况参数的变化,得到特征统计量与工况参数之间的连续函数关系;基于不同工况下的特征统计,构建自适应阈值模型。将该方法用于不同转速下的轴承健康监测,结果表明,当转速超过某一个较小的值时,该方法有效。

Bearings health monitoring under varying operation conditions using RVM and adaptive threshold model

Operation conditions usually change when rotating machinery works. The changing operational conditions, as well as machine fault, could make the mechanical vibration characteristics change and cause diagnosis errors. This paper presents a new method for the health monitoring of bearings under changing operational conditions. In this method, Relevance Vector Machines (RVMs) are used for regression of the relationships between the adaptive parameters of the threshold model and the statistical characteristics of vibration features. And the adaptive threshold model is constructed based on these relationships. This method is validated on bearings running at changing speeds. The monitoring results show that this method is effective as long as the rotational speed is higher than a relative small value.