基于主曲线相似度的轴承健康状态评估方法

Evaluation Method of Bearing Health State Based on Similarity of Principal Curve

为了更有效地评估滚动轴承性能退化程度,提出了基于流形空间主曲线相似度的状态评估方法。首先,结合轴承振动信号自身特点,进行高维特征提取,利用流形学习算法拉普拉斯特征映射(Laplacian eigenmaps,简称LE)将原高维特征空间转换至低维空间;其次,利用软-K主曲线算法提取样本主曲线;最后,结合离散Frechet距离做出状态评估曲线。通过滚动轴承全寿命实验进行对比分析,所提方法相对隐马尔科夫链模型(hidden Markov model,简称HMM)、深度信念网络(deep belief networks,简称DBN)等方法,能更早地发现设备的早期故障,且可以对滚动轴承健康状态进行定量评估。

In order to more effectively assess bearing performance degradation degree,a rolling bearing state evaluation method is proposed based on the similarity of the main manifold space curve.The high dimensional feature of vibration signal is extracted and then is converted to low dimensional space using the manifold learning algorithm of Laplacian eigenmaps(LE).Then samples from the curve according to soft-Kprincipal curve algorithm are combined with the discrete Frechet distance to plot the condition assessment curve.Comparing with the hidden Markov model(HMM),deep belief network(DBN)method,small breakdown of equipment could be detected earlier,and the health state quantitative assessment of a rolling bearing is achieved.