基于ARMA模型双谱分布与FCM方法的轴承故障识别

针对滚动轴承信号非线性和非高斯性的特点,提出了基于自回归滑动平均(ARMA)模型双谱分布特征与模糊c均值(FCM)聚类分析的故障识别方法.首先,利用经验模态分解改善信号,对获得的信号主分量建立ARMA模型;然后,对ARMA模型进行双谱分析;最后,以阈值化的双谱分布二值图为特征向量,借助FCM聚类算法构建类模板与最近邻模板分类器,实现故障识别.滚动轴承实例诊断结果表明,该方法能准确地判断轴承的实际性态,是一种有效的故障识别方法.

Fault Identification of Bearings Based on Bispectrum Distribution of ARMA Model and FCM Method

According to the nonlinear and non-Guassian characteristics of vibration signals of rolling bearings,a novel fault identification method based on the bispectrum distribution feature of auto-regressive moving average(ARMA) model and on the cluster analysis of fuzzy c-means(FCM) method is proposed.In this method,first,original vibration signals are modulated via the empirical mode decomposition(EMD),and an ARMA model of principal signal components is established.Then,a bispectrum estimation of the ARMA model is implemented.Finally,the binary images extracted from the bispectrum distribution are taken as the feature vectors and are used to construct a classifier of the class templates and the smallest-distance templates via the FCM clustering,thus implementing the fault identification successfully.Application results in the fault diagnosis of rolling bearings demonstrate that the proposed method is effective because it can accurately determine the actual conditions of rolling bearings.