支持向量机理论简单, 实用性很强, 被大量应用于故障诊断问题中. 在分析支持向量机参数对分类结果影响的过程发现, 不恰当的参数选择往往带来较差的分类结果. 采用启发式优化方法可以避免人为选择的不足, 但单纯以等效间隔距离为寻优目标又会较大概率出现“过学习”现象. 为降低整体结构风险, 将等效间隔距离、支持向量数量和错分率等同时作为优化目标, 提出了一种基于粒子群的多目标支持向量机方法, 并采用定时重启、动态学习因子等策略提升算法全局寻优能力. 最后将其应用于多故障强关联耦合的复杂柴油机故障诊断问题中. 实验结果表明, 该方法可以有效解决少样本、不完备或不确定征兆的柴油机异响故障诊断问题, 筛选得到的综合最优解更符合人们的期望.
Support vector machines have a simple theory and strong practicability, which are thus widely used in fault diagnosis. In the process of analyzing the influence of support vector machine parameters on classification results, it is found that inappropriate parameter selection often leads to poor classification results. The adverse effects of artificial selection can be avoided by using a heuristic optimization method. However, taking the equivalent interval distance as the optimization goal is prone to result in “over learning”. Taking the equivalent interval distance, the number of support vectors and the misclassification rate as optimization objectives at the same time, this study proposes a multi-objective support vector machine method based on particle swarm optimization. The strategies of timed restart and dynamic learning factor are used to improve the global optimization ability of the algorithm. In this way, the overall structural risk can be reduced. The proposed method is applied to the fault diagnosis of a complex diesel engine with strong correlation and coupling of multiple faults. The experimental results show that this method can effectively diagnose the fault of abnormal noise from diesel engines in the case of small samples and incomplete or uncertain symptoms, and the comprehensive optimal solution obtained by screening is more in line with people’s expectations.