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。
Operation conditions usually change when rotating machinery works. The changing operational conditions and machine fault can make the mechanical vibration characteristics change and cause diagnosis errors, so a new method for the health monitoring of bearings under changing operational conditions was proposed. In this method, the RVMs (Relevance Vector Machines) were used for obtaining the continuous function relationships between the adaptive parameters of the threshold model and the characteristic statistics of vibration features. Based on the characteristic statistic in different operation conditions, the adaptive threshold model was constructed. This method was used for bearings health monitoring at different revolving speed. Monitoring results show that this method is effective only when the rotational speed is higher than a relative small value.