摘要
针对传统Boosting算法在训练样本不均衡数据情况下不能较好地实现转子系统故障诊断的问题,提出了一种基于代价敏感度框架的Boosting故障诊断算法CS-Boosting。该算法建立了一个代价敏感损失函数,通过先验概率公式计算正样本与负样本的惩罚因子,并通过决策规则的训练使代价损失函数最小化。将该算法应用到滚动轴承故障诊断中,并与传统的Adaboost算法进行对比。试验结果表明,在转子系统不能获取更多故障数据的情况下,该算法的故障诊断性能较其他算法有明显的提高。
A novel framework of cost-sensitive boosting algorithm is presented,which overcomes the drawbacks of traditional boosting algorithm which has low performance with unbalanced training dataset.A loss function is constructed,and the loss function is minimized by training decision rules.The new framework is used for rolling bearing system fault diagnosis.The comparison experiments are made with traditional Adaboost algorithm.Simulation results show that the proposed algorithm has better performance than the traditional one,when more fault dataset of rolling bearing system cannot be obtained.
出处
《振动．测试与诊断》
EI
CSCD
北大核心
2013年第1期111-115,169,共5页
Journal of Vibration,Measurement & Diagnosis
基金
国家自然科学基金资助项目(51075330
50975231
61003137)
