基于IGSA优化的LSSVM制冷系统故障诊断研究

为提高制冷系统故障诊断的准确率,提出一种基于改进引力搜索算法(IGSA)优化的最小二乘支持向量机(LSSVM)的制冷系统故障诊断方法。首先,引入粒子群算法的速度更新机制对引力搜索算法进行改进,增加粒子的记忆性和信息共享能力,提高了算法的收敛速度和搜索精度；其次,利用IGSA对LSSVM的核参数与正则化参数进行优化,得到最优的IGSA-LSSVM故障诊断模型。最后,利用故障模拟实验台模拟制冷系统的四种典型故障,将优化好的LSSVM模型对其进行分类识别,并与标准LSSVM、GSA-LSSVM和PSO-LSSVM模型进行比较。仿真结果表明,基于IGSA优化的LSSVM方法具有良好的辨识能力和泛化能力,能够更好地对制冷系统故障进行诊断。

Fault diagnosis of LSSVM refrigeration system based on IGSA optimization

To improve the diagnosis accuracy of refrigeration system faults, an optimized Least Squares Support Vector Machine (LSSVM) based fault diagnosis method using the improved gravity search algorithm (IGSA) was proposed. Firstly, to increase the memory and information sharing ability of the particles, the gravitational search algorithm was further developed using the speed updating mechanism in particle swarm optimization algorithm, so that its calculation convergence and the search accuracy were improved. Through optimizing the kernel parameters and regularization parameters of LSSVM using IGSA, the proposed IGSA-LSSVM fault diagnosis model was then developed. Finally, using the experimental data obtained from a real refrigeration system, four typical faults of the refrigeration system were successfully identified by the optimized IGSA-LSSVM model. The fault diagnosis results, in comparison with that using the standard LSSVM, GSA-LSSVM and PSO-LSSVM models, showed that the proposed IGSA-LSSVM method was better as expressed in terms of its identification ability and generalization ability.