摘要
齿轮箱的振动信号当中包含齿轮运转过程中的所有特征信息,通过采集分析振动信号即可掌握设备当前运转状态。采集齿轮箱振动信号的方法有很多,利用激光自混合干涉原理实现的非接触式测量手段具有极其独特的优势:一是该方法能采集到齿轮箱故障早期的特征信号,以便及时采取对应措施;二是该方法在测量过程中无需破坏齿轮箱箱体表面即可得到检测所需的特征信息;三是相比于安装加速度传感器,该方法对箱体附着的反光镜片产生的影响很微小,可忽略不计。在实验过程中,人为制作行星轮磨损故障并采集故障特征信号,然后使用希尔伯特-黄变换(Hilbert-Huang transform,HHT)信号处理方法对采集到的行星轮的故障振动信号进行提取,并分析HHT所得到的各阶固有模态函数(IMF)分量以及希尔伯特谱。从希尔伯特谱中能够分析得出齿轮磨损故障的周期,所得结果与理论值对比吻合良好,说明该方法能够有效地检测齿轮箱振动故障。
The vibration signal of the gearbox contains all the characteristic information during gear operation.The current running state of the equipment can be mastered by collecting and analyzing vibration signals.There are many methods for collecting gearbox vibration signals.The noncontact measurement method,based on the principle of laser selfmixing interference,has extremely unique advantages.First,this method can collect the early characteristic signals of gearbox fault and take corresponding measures in time.Second,the method can obtain the required characteristic information without damaging the gearbox’s surface.Third,compared with the installation of an acceleration sensor,the influence of this method on the reflector attached to the gearbox is very small and can be ignored.The planetary gear wear fault is artificially made,and the fault characteristic signal is collected during the experiment.Furthermore,the fault vibration signals of planetary gears are extracted and the intrinsic mode function(IMF)components of each order and Hilbert spectrum are analyzed using the Hilbert-Huang transform(HHT)signal processing method.The Hilbert spectrum can be analyzed during the period of gear wear fault.The results are in good agreement with the theoretical values,showing that this method can effectively detect gearbox vibration faults.
作者
周旭明
徐鹏
赵芬妮
李鹏飞
Zhou Xuming;Xu Peng;Zhao Fenni;Li Pengfei(Logistics University of People’s Armed Police Force,Tianjin 300309,China)
出处
《激光与光电子学进展》
CSCD
北大核心
2022年第23期136-141,共6页
Laser & Optoelectronics Progress
基金
中国人民武装警察部队后勤学院应用研究项目(WHY202007)。
关键词
仪器、测量与计量
振动信号
故障诊断
自混合干涉技术
希尔伯特-黄变换
instrumentation
measurement and metrology
vibration signal
fault detection
self-mixing interference technology
Hilbert-Huang transform