针对旋转机械故障诊断中采集到的振动信号存在强烈噪声及野值干扰,故障特征提取后,利用传统的支持向量机( support vector machine,SVM)进行模式识别会造成最优超平面的模糊性,影响分类效果,引入模糊C均值聚类算法( fuzzy C-means,FCM)与支持向量机结合进行故障诊断. FCM用来求解样本模糊隶属度,但其迭代求解聚类中心及样本模糊隶属度矩阵时容易陷入局部最优,而粒子群算法( particle swarm optimization,PSO)具有全局优化搜索的优点. 基于此,提出了基于改进模糊支持向量机( fuzzy support vector machine,FSVM)的旋转机械故障诊断算法. 首先,利用经验模态分解( empirical mode decomposition, EMD)提取故障信号的能量特征指标;然后,由PSO优化FCM求解样本的模糊隶属度;最后,将模糊隶属度引入SVM,构建改进的模糊支持向量机模型,并实现故障判别. 实验结果表明:改进的FSVM比传统的FSVM算法有更好的抗造性能以及分类效果.%In fault diagnosis of rotating machinery, the strong noise and outliers interference are usually contained in the vibration signals. After fault feature extraction, the method of traditional support vector machine ( SVM ) for the pattern recognition causes the fuzzy of optimal hyperplane and affects the classification results. So a fuzzy C-means ( FCM) clustering algorithm was introduced in this paper. FCM was used to solve the problem of fuzzy membership. However, the FCM had its own defects. The clustering result was sensitive to the initial center, and often cannot achieve the result of the global optimal. Improved by particle swarm optimization ( PSO) which has advantages of global optimization search, the FCM achieved better fuzzy memberships for each sample. So, the fault diagnosis algorithm of rotating machinery based on the improved fuzzy support vector machine ( FSVM) was proposed. First, fault features were extracted by using the empirical mode decomposition ( EMD) . Second, the problem of fuzzy membership was solved by using FCM which was optimized by PSO. At last the fuzzy memberships were put into SVM,the improved FSVM was founded and fault recognition was realized. Results of the experiment show that the improved FSVM has better anti-noise performance and the classification effect is better than that of the traditional FSVM algorithm.
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