24种特征指标对轴承状态识别的性能研究

Studying on Property of 24Characteristic Indexes to Bearing State Recognition

要实现机械设备的故障诊断,关键是要找到对设备状态敏感性和聚类性强的特征指标。针对如何筛选出满足上述特点的指标,提出结合投影寻踪方法研究24种特征指标对轴承状态识别的敏感性和聚类性;以内圈故障为例,构造其故障振动的数学模型,计算得到24种特征指标并将其投影,提出最佳投射方向矩阵,研究在最佳投影方向矩阵下24种特征指标的投影分布特征;提出用极差系数、平均差系数、离散系数、主轴线相对系数和均值系数来研究24种特征指标对不同故障的敏感性和聚类性;借助美国西储大学轴承数据中心网站公开发布的轴承探伤测试数据集中的内圈故障进行验证。该方法能够为轴承的故障诊断筛选优质特征指标,保证故障识别的及时性和诊断准确性。

Using a method combining the projection pursuit method,the sensitivity and clustering to bearing status recognition of 24 characteristic indexes was studied.Using an inner ring fault as an example,a mathematical model was constructed to simulate fault signals,and 24 characteristic indexes were generated,then projected.The best projection direction matrix was put forward,and the projection distribution characteristic was studied.The range coefficient,coefficient of mean deviation,coefficient of dispersion,main axis of the relative coefficient and coefficient of mean value were proposed to study the sensitivity and clustering to different failure status of the 24 characteristic indexes projection.It can be validated based on of the inner ring fault test data publicly released on the Case Western Reserve University Bearing Data Center website.This method can be used to screen high quality characteristics for bearing fault diagnosis,which can guarantee prompt fault recognition and accurate diagnosis.