基于混沌人工蜂群算法优化的SVM 齿轮故障诊断

为克服支持向量机中模型参数的随意选择对分类性能造成的不利影响, 提出了基于混沌人工蜂群算法的支持向量机(CABC鄄SVM: Chaotic Artificial Bee Colony algorithm of Support Vector Machine)参数优化方法。该方法采用Logistic 混沌映射初始化种群和锦标赛选择策略, 对支持向量机的惩罚因子和核函数参数进行优化时以分类准确率作为适应度函数。通过UCI 标准数据集实验证明, CABC 具有较强的局部和全局搜索能力, 其优化的支持向量机可在很大程度上克服局部极值点, 从而获取更高的分类准确率, 并有效缩短了搜索时间。将该方法应用于实际齿轮故障诊断中, 采用小波相对能量作为特征输入支持向量机, 分类准确率达到99. 4%, 验证了该方法的可行性和有效性。

SVM Optimization Based on Chaotic Artificial Colony Algorithm Gear Fault Diagnosis

In order to overcome the adverse effects on the performance of classification of SVM(Support Vector Machine) model parameters in the random selection, based on chaotic CABC-SVM ( Artificial Bee Colony Algorithm of Support Vector Machine) parameters optimization method is proposed. CABC algorithm uses the Logistic chaotic mapping initialization population and tournament selection strategy, further improves the artificial bee colony algorithm convergence speed and optimization precision, the classification accuracy as the fitness function when using the algorithm of penalty factor and kernel function parameters of SVM was optimized. UCI standard data sets experiments show that CABC algorithm has strong local and global search ability, the optimization of SVM can largely overcome local extremum points to obtain a higher classification accuracy, and can effectively shorten the search time. The method was applied to actual gear fault diagnosis, energy wavelet was used as the feature input SVM, classification accuracy rate reached 99. 4%, verified the feasibility and effectiveness of the improved method in this paper.