混沌粒子群优化模糊聚类的旋转机械故障诊断

Fault diagnosis of rotating machinery based on fuzzy clustering optimized by chaos embedded particle swarm optimization

提出基于混沌粒子群优化加权模糊聚类的旋转机械故障诊断算法。该算法用混沌粒子群算法取代传统的梯度下降法,优化加权模糊C-均值算法的各个参数,并依据聚类有效性指标确定最优聚类数及聚类中心。应用表明,混沌粒子群算法有效提高了模糊聚类分析的收敛速度和精度,提高了旋转机械故障诊断的准确率。

A method of weighted fuzzy clustering optimized by chaos embedded particle swarm algorithm （CPSO） is put forward and applied in vibration fault diagnosis of rotating machinery. In the method, CPSO is used to displace the traditional stochastic-gradient algorithm to optimize parameters of weighted fuzzy Cmeans （WFCM）. The best clustering num and clustering centers are automatically attained according to clustering validity function. The experimental results show that the method effectively increases the con- vergence velocity and precision of WFCM and so does the correctness rate of fault diagnosis for rotating machinery.