齿轮剥落故障特征识别方法研究  

Study on the Identification Method of Gear Spalling Fault

作　　者：吴胜利[1] 邵毅敏[2] 邢文婷[3] 简晓春[1] Wu Shengli;Shao Yimin;Xing Wenting;Jian Xiaochun(College of Traffic and Transportation,Chongqing Jiaotong University,Chongqing 400074,China;State Key Laboratory of Mechanical Transmission,Chongqing University,Chongqing 400044,China;School of Management,Chongqing Technology and Business University,Chongqing 400067,China)

机构地区：[1]重庆交通大学交通运输学院,重庆400074 [2]重庆大学机械传动国家重点实验室,重庆400044 [3]重庆工商大学管理学院,重庆400067

出　　处：《机械传动》2019年第8期116-119,共4页Journal of Mechanical Transmission

基　　金：国家自然科学基金(51705052);重庆市教委科学技术研究项目(KJ1705141)

摘　　要：齿轮在啮合过程中,轮齿表面不可避免地会出现点蚀、剥落等故障,严重影响齿轮传动的稳定性和可靠性。基于齿轮时变啮合刚度模型和6自由度剥落故障齿轮动力学模型,研究了利用Matlab小波工具箱构造与信号对应的自适应小波的方法,阐明了振动信号的时频特征变化规律,并通过试验验证了构建自适应小波方法的正确性和对齿轮表面剥落缺陷识别的有效性,为在黑箱状态下有效识别齿轮缺陷以及分析缺陷尺寸提供了必要的理论基础和实践支撑。palling and pitting are prone to be produced on the tooth surfaces in the meshing process,which seriously affects the stability and reliability of gear meshing. The time-varying meshing stiffness modeland six-degree-of-freedom model of gear meshing are established, the Matlab wavelet toolbox is used to gener-ate an adaptive wavelet which is similar to the original signal. The time frequency characteristic change rule ofvibration signal is illustrated. Agreement between the results of the dynamic model and the experimental resultsvalidates the effectiveness of the generated adaptive wavelet method which is used to analyze the spalling defect.The results provide theoretical basis and practical support for the gear defect size identification in black-boxcondition.

关 键 词：齿轮 剥落缺陷 自适应小波 识别分析 

分 类 号：TH132.41[机械工程—机械制造及自动化]