基于失衡样本特性过采样算法与SVM的滚动轴承故障诊断

针对传统支持向量机(SVM)算法在滚动轴承故障诊断领域中,对失衡数据集效果不佳、对噪声敏感以及对本身参数依赖较大等缺点,提出一种基于样本特性的过采样算法(OABSC)。该算法利用改进凝聚层次聚类将故障样本分成多个簇;在每个簇中综合考虑样本距离、近邻域密度对"疑似噪声点"进行识别、剔除,并将剩余样本按信息量进行排序;紧接着,在每个簇中采用K^*-信息量近邻域(K^*INN)过采样算法合成新样本,以使得数据集平衡;模拟3种不同失衡比下的轴承故障情况,并采用粒子群算法优化了SVM分类器的参数。经试验证明:相比已有算法,OABSC算法能更好地适用于数据呈多簇分布且失衡的轴承故障诊断领域,拥有更高的G-mean值与AUC值以及更强的算法鲁棒性。

Rolling bearing fault diagnosis based on imbalanced sample characteristics oversampling algorithm and SVM

Aiming at the shortcomings of the standard support vector machine(SVM) in the field of rolling bearing fault diagnosis, such as poor performance on imbalanced datasets, sensitivity to noise, and heavy dependence on its own parameters, an oversampling algorithm based on sample characteristics(OABSC) was proposed.First,improved agglomeration hierarchical clustering was used to divide the failure samples into multiple clusters. Then, the sample distance and the neighborhood density in each cluster were comprehensively considered to identify and remove "suspected noisy points", and sort the remaining samples according to the amount of information. Further, K^*-information nearest neighbors(K^* INN) oversampling algorithm in each cluster was utilized to synthesize new samples to balance the dataset. Finally, bearing failures at three different imbalance ratios were simulated and the parameters of the SVM classifiers were optimized by using particle swarm optimization(PSO). The experiments show that, compared with the existing algorithms, the proposed OABSC algorithm is better applicable to the field of bearing fault diagnosis where the data is distributed in multiple clusters and is imbalanced.It has higher G-mean value and AUC value, and stronger algorithm robustness.