摘要
实际工程中滚动轴承受工况、运行环境等因素影响,获取的数据不易满足传统机器学习中训练数据和测试数据独立同分布且训练样本足够多的条件,直接影响故障诊断率。为此,提出一种改进TrAdaBoost多分类算法的滚动轴承故障诊断方法。引入大量辅助标记数据和少量目标标记数据组成联合训练集使训练样本足够多,并应用异分布加权随机抽样对TrAdaBoost迭代过程中的联合训练集进行重组,获得与测试集"近似同分布"的优化联合训练集,降低不同分布数据间的差异性。其次,将迭代结束后的内部分类器模型作为输出,从而改变TrAdaBoost的输出机制使其适应多分类任务。最后,为削弱随机抽样对诊断结果的影响,对多次抽样得到的结果进行一致性投票以得到最终诊断结果。实验结果证明了所提方法的可行性与有效性。
In engineering practice, rolling bearings are affected by working conditions and runtime environment. The acquired data can’t easily satisfy conditions of training data and tested ones being independent and having the same distribution, and enough training samples in traditional machine learning to directly affect fault diagnosis rate. Here, a rolling bearing fault diagnosis method based on the improved TrAdaBoost multi-classification algorithm was proposed. Firstly, a large amount of auxiliary labeled data and a small amount of target labeled data were introduced to form a joint training set, and make training samples enough. The heterogeneous distribution weighted random sampling was used to reconstruct the joint training set in TrAdaBoost iteration process, acquire the optimal joint training set with the approximate same distribution as that of the tested set, and reduce the diversity among different distribution data. Secondly, the internal classifier model after iteration ending was taken as output to change the output mechanism of TrAdaBoost, and make it adapt to the multi-class task. Finally, to weaken effects of random sampling on diagnosis results, results of multiple samplings were voted with consistence to get the final diagnosis results. The test results verified the feasibility and effectiveness of the proposed method.
作者
陈仁祥
陈思杨
杨黎霞
徐向阳
董绍江
唐林林
CHEN Renxiang;CHEN Siyang;YANG Lixia;XU Xiangyang;DONG Shaojiang;TANG Linlin(School of Mechantronics and Vehicle Engineering,Chongqing Jiaotong University,Chongqing 400074,China;The State Key Laboratory of Mechanical Transmission,Chongqing University,Chongqing 400030,China;Chongqing Radio & TV University,Chongqing 400052,China)
出处
《振动与冲击》
EI
CSCD
北大核心
2019年第15期36-41,48,共7页
Journal of Vibration and Shock
基金
国家自然科学基金(51305471)
机械传动国家重点实验室开放基金项目(SKLMT-KFKT-201710)
重庆市留学人员回国创业创新支持计划创新项目(CX2018116)
重庆市技术创新与应用示范项目(cstc2018jscx-msybX0012)
城市轨道交通车辆系统集成与控制重庆市重点实验室开放基金(CKLURTSIC-KFKT-201809)
交通工程应用机器人重庆市工程实验室开放基金(CELTEAR-KFKT-201803)
重庆市教育委员会科学技术研究资助项目(KJ1500516)
重庆交通大学硕士研究生科研创新项目(2018S0138)
