基于改进LE和约束种子K均值的半监督故障识别

Semi-supervised fault identification based on improved Laplace feature mapping and constraint seed K-means

为充分利用少量有标记样本蕴含的重要信息,在拉普拉斯特征映射(LE)算法基础上,对标记样本点进行置信度约束,提出了改进的LE算法及基于该算法的半监督故障诊断模型。该模型采用改进的LE算法,直接从原始高维振动信号中提取最敏感的低维流形特征,随后将其输入到基于约束种子K均值算法构建的分类器,从而以可视化的聚类结果标识机械设备的运行状态。与核主成分分析、核判别分析等经典算法进行比较,该模型能明显提高轴承故障类型和滚动体故障严重性的识别性能。

Aiming at making full use of the important messages contained in a small number of marked samples.The Laplacian eigenmap(LE)algorithm was improved by implementing confidence constraints on marked sample points.The semi-supervised fault diagnosis model based on the improved LE algorithm was presented.This model utilized the improved LE algorithm to extract the most sensitive low-dimensional manifold features from the raw high-dimensional vibration signals directly.Subsequently,they were fed into the classifier based on the constraint seed K-means algorithm.Thus,the operating conditions of mechanical equipment were identified by visual clustering results.Compared with the Kernel principal component analysis and the Kernel discriminant analysis,the model obviously improves the recognition performance of bearing fault types and ball fault severities.