基于故障敏感分量和改进K近邻分类器的故障状态识别

针对故障状态下的滚动轴承振动信号非线性非平稳性强、噪声干扰大导致的故障敏感特征提取难的问题,在对轴承振动信号进行局域均值分解(local mean decomposition, LMD)的基础上,提出了一种基于故障敏感分量的特征提取与改进K近邻分类器(K-nearest neighbor classifier, KNNC)的故障状态辨识方法。该方法采用相关系数法对LMD分解出的振动分量进行故障敏感性的量化表征,然后对筛选出的信号分量进行时域/频域的特征提取,构建不同故障状态下的特征样本集。为加快故障状态识别速度,排除不良样本的影响,提出一种基于二分K均值聚类的改进KNNC算法,精简了大容量的训练样本,有效去除不良特征样本和干扰点。实验结果表明,以敏感分量特征作为输入的改进KNNC算法能够快速准确地识别轴承不同故障状态。

Fault state identification method based on fault sensitive components and improved KNNC

To solve the problem of sensitive feature extraction from the non-stationary and nonlinear vibration signals of rolling bearing, local mean decomposition (LMD) was carried out. and the time/frequency domain features were extracted from the sensitive fault components quantified by the correlation coefficient method. Then, the feature sets of different faults states were established and used to train the state classifier. In order to achieve the higher accuracy of bearing fault states identification, an improved K-nearest neighbor classifier (KNNC) algorithm based on dichotomy K-means clustering was proposed, in which the big training samples were simplified, and the bad samples and interference points were effectively removed. Finally, the effectiveness of the method was verified through diagnostic analysis of experimental data of bearings.