基于人工蜂群优化核主元分析故障检测方法

Fault Detection Method with Kernel Principal Component Analysis Based on Artificial Bee Colony Optimization

核主元分析方法（Kernel Principal Component Analysis,KPCA）能有效地捕捉数据的非线性特征,其故障检测能力很大程度上取决于核参数的选择。常用的核函数有多项式核函数和高斯径向基核函数等。该方法将多项式核函数和高斯径向基核函数进行线性组合,结合两者优点得到混合核函数,使用故障检测率作为优化目标的适应度函数,通过人工蜂群（Artificial Bee Colony Algorithm,ABC）优化算法对KPCA核参数的选取进行优化。将该方法应用到电主轴的转子不平衡故障分析中,对信号进行时域分析,利用KPCA对样本数据进行非线性特征提取,根据主元特征计算出的T2和SPE统计量实现故障检测。经过对实验数据分析表明,ABC优化算法较二分法、粒子群等优化算法能更有效地提高故障检测率。

KPCA could effectively obtain the nonlinear characteristic of the data, and the selection of kernel function parameters directly affects the fault detection capability of the kernel function itself. A linear combination of the polynomial kernel function and Gaussian radial basis kernel function, which are common kernel functions, is as the mixture of kernels. The fault detection rate is taken as the fitness function for optimizing the goal. The kernel parameters of KPCA are optimized by the artificial bee colony algorithm. The approach is applied to detect the fault of rotor unbalance, and firstly the motorized spindle is analyzed in time domain, then the nonlinear characteristics of sample data is extracted by KPCA, thus the fault could be detected on-line by monitoring T2 and squared prediction error（SPE）. According to the analysis of experimental data, ABC optimization algorithm could effectively improve the fault detection rate than the dichotomy and particle swarm optimization algorithm.