用于转子故障诊断的改进小波聚类算法

Improved Wave Cluster Method for Rotor Fault Diagnosis

为了快速准确地实现转子故障的模式识别与分类,提出了改进小波聚类方法。首先,从转子振动信号中提取峭度指标、功率谱重心和小波能谱熵三个特征向量;其次,量化特征空间,提取显著网格单元信息;然后,对显著网格单元内数据信息进行小波变换实现去噪处理;最后,应用广度优先搜索方法实现聚类。在改进小波聚类过程中,信息储存表的建立降低了空间复杂度,并使得原始数据与聚类结果建立了映射关系。应用广度优先原则搜索相邻的显著网格单元实现聚类,降低了聚类算法的复杂度。实验验证与比较说明,改进小波聚类算法能够扩展应用到高维数据空间,并且降低了高维数据空间的算法复杂度,提高了转子故障诊断的效率和正确率。

In order to identify and classify the rotor faults quickly and accurately,a method of improved wave cluster is presented in this paper.Firstly,three feature vectors are extracted from the vibration signal of the rotor,which are kurtosis,power spectral centroid and wavelet energy entropy,respectively.Secondly,the feature space is quantized and significant grid cell information is extracted before the wavelet transform is performed on data to remove noise.Finally,based on the breadth-first search(BFS)algorithm,adjacent significant grid units is connected to achieve clustering.In the process of improved wavelet clustering,the space complexity can be reduced due to the establishment of the information storage table,and the mapping relationship between the original data and the clustering results can be established.BFS algorithm is applied to search the connected unit,therefore,the complexity of clustering algorithm is reduced.The improved wave cluster method can be extended to high dimensional data space.Through experimental verification and comparison,the results show that the algorithm complexity of high dimensional data space is reduced,and the efficiency and accuracy of rotor fault diagnosis are improved.