摘要
针对模糊C均值聚类算法容易陷入局部极值和对初始值敏感的缺点,提出了一种粒子群优化模糊聚类算法,该算法利用粒子群优化算法寻找最优聚类中心,运用WFCM进行加权模糊聚类,能较大提高聚类的有效性;将该算法应用于煤气鼓风机组振动故障诊断中进行诊断仿真,结果表明:该算法较大提高了故障诊断的正确率。
According to the fault that fuzzy C-means clustering algorithm is easily involved in local extreme and is sensitive to initial value, a kind of PSO fuzzy clustering algorithm is proposed, this algorithm searches the optimal clustering center based on PSO algorithm, uses WFCW to conduct weighted fuzzy cluster and is able to relatively more largely improve the validity of the cluster. This algorithm is used in diagnosis simulation in the fault diagnosis of gas blower group vibration and the results show that this algorithm can relatively more largely improve the accurate rate of fault diagnosis
出处
《重庆工商大学学报（自然科学版）》
2013年第2期37-41,共5页
Journal of Chongqing Technology and Business University:Natural Science Edition
关键词
粒子群模糊聚类
煤气鼓风机组
故障诊断
particle swarm optimization （PSO）
gas blower group
fault diagnosis
