改进的蜂群LS-SVM故障预测

Improved Artificial Bee Colony LS-SVM Fault Prediction

为了提高基于最小二乘支持向量机的故障预测精准度,提出了AFS-ABC算法,用于组合优化LS-SVM的规则化参数C和宽度参数σ。该算法将鱼群算法AFS简化模型中人工鱼的寻优更新方法引入到蜂群算法中,以互补优势、互克不足。通过100维Ackley函数验证了该算法在优化精度和搜索速度上较AFS算法与ABC算法的优越性,并以某航空电子系统电源模块记录电压数据序列的前40个作为LS-SVM模型的训练集,后15个作为测试集,利用MAT-LAB的LS-SVM工具箱进行状态预测仿真。结果表明,AFS-ABC算法较好地改善了LS-SVM的预测精度,同时解决了局部极值和寻优结果精度低的问题。

Under the background of condition-based maintenance in the arming maintenance, in allusion to problem of the less equipment data swatch, the fault prognostic method based on least squares support vec- tor machine is studied. The idea of artificial fish swarm algorithm is used to replace the function of the em- ployed bees in the artificial bee colony , therefrom AFS-ABC algorithm is advanced and then used to opti- mize the parameter of LS-SVM. The LS-SVM is trained by the front forty voltage data sequence swatches of the power supply module in avionics subsystem, and tested by the rear fifteen data sequence swatches. The simulation is done by using the MATLAB LS-SVM toolbox. The result of the simulation shows that the use of this method can prognosticate the arming fault and preferably enhance the capability of LS- SVM.