基于随机森林的滚动轴承故障辨识方法研究  

Fault identification method of rolling bearing based on random forest

作　　者：王兰兰[1] 朱捷 周正平 常兆庆 WANG Lan-lan;ZHU Jie;ZHOU Zheng-ping;CHANG Zhao-qing(Zhengzhou Railway Vocational&Technical College,Zhengzhou 451460,China;School of Management Engineering,Zhengzhou University of Aeronautics,Zhengzhou 450046,China;School of Automation,Nanjing University of Aeronautics and Astronautics,Nanjing 211106,China;Jiangsu Shuguang Optoelectronics Co.,Ltd.,Yangzhou 225000,China)

机构地区：[1]郑州铁路职业技术学院,河南郑州451460 [2]郑州航空工业管理学院管理工程学院,河南郑州450046 [3]南京航空航天大学自动化学院,江苏南京211106 [4]江苏曙光光电有限公司,江苏扬州225000

出　　处：《机电工程》2021年第12期1599-1604,共6页Journal of Mechanical & Electrical Engineering

基　　金：国家自然科学基金资助项目(51775272)。

摘　　要：滚动轴承工作时的故障信息难以获取,且其故障信息的辨识也存在困难,针对这一问题,提出了一种基于随机森林(RF)的滚动轴承故障辨识方法。首先,采集了滚动轴承的原始振动信号,并基于时域统计指标提取出了其原始振动数据的特征向量;然后,建立了基于随机森林的轴承故障辨识模型,同时利用测试集验证了故障分类结果,给在测试集分类过程中识别率较高的决策树赋予了较大的权重,使得对应的决策树在未来的分类过程中可以发挥更大的作用;最后,用验证集验证了最终的分类结果,通过多域多通道的滚动轴承故障特征数据集验证了所提方法的有效性。研究结果表明:在不同转速和变工况条件下,基于随机森林的滚动轴承故障诊断方法都能取得良好的轴承故障辨识效果,其分类准确率达到96%;与采用BP神经网络(BPNN)、K-近邻算法(KNN)、支持向量机(SVM)传统分类器的辨识结果相比,采用随机森林辨识方法的分类准确率明显更高。Aiming at the problem of difficulty in obtaining and identifying the fault information when the rolling bearing was working,a random forest(RF)based fault identification method for rolling bearings was proposed.First,the rolling bearing vibration signal was collected and the feature vector of the original vibration data based on the time-domain statistical indicators was extracted.Then,the fault identification model based on random forest is established,and the test set was used to verify the classification results.The decision tree with a higher recognition rate was given a greater weight,so that the corresponding decision tree could play a greater role in the future classification process.Finally,the verification set was used to verify the final classification result.The effectiveness of the proposed method was verified through a multi-domain and multi-channel rolling bearing fault feature data set.The research results show that the fault diagnosis of rolling bearings based on random forest can achieve good identification results under different speeds and variable operating conditions,and the classification accuracy is as high as 96%;comparing with the traditional classification of back propagation(BP),k-nearest neighbor classification(KNN),and support vector machines(SVM),the comparison results of the detectors show that the accuracy of random forest classification is significantly higher than that of traditional classifiers at different speeds.

关 键 词：滚动轴承 故障辨识 特征提取 随机森林 

分 类 号：TH133.33[机械工程—机械制造及自动化] TH165.3