基于平稳工况数据截取的RV减速器故障诊断方法

A Fault Diagnosis Method for RV Reducer Based on Stationary Condition Data Capturing

中文关键词: 工业机器人  旋转矢量减速器  故障诊断  同步压缩变换  脊线提取

英文关键词: industrial robot  rotary vector (RV) reducer  fault diagnosis  synchro-squeezing transform  ridge extraction

中文摘要:

      旋转矢量（Rotary Vector, RV）减速器广泛应用在工业机器人重载关节处，直接影响着工业机器人的工作精度和稳定性。与一般旋转机械设备不同，工业机器人完成各项工作任务时，关节处RV减速器做往复变转速转动，转速的变化会使特征频率、统计指标等产生时变效应，导致故障信息难以提取，为故障诊断带来挑战。文中结合工业机器人RV减速器的运动特点，提出基于平稳工况数据截取的RV减速器故障诊断方法。首先通过引入压缩范围限制因子获取清晰的时频谱图；然后进行基于快速路径优化的脊线提取，并利用构建的滑动窗峰–峰值和均值指标截取脊线平稳段，获取所需的平稳工况数据；最后通过对平稳工况数据进行包络谱分析实现故障诊断。利用RV减速器往复运动振动数据对提出的诊断方法进行验证。结果表明，该方法可实现对平稳工况数据的准确截取，克服了转速变化的影响，成功提取了故障信息。

英文摘要:

      Rotary vector (RV) reducers are widely used at heavy-duty joints, which directly affect the working accuracy and stability of industrial robots. Different from the general rotating machines, when the industrial robot performs various tasks, the RV reducer at the joint rotates at reciprocating variable speed. The change of rotation speed will cause time-varying effects on characteristic frequency and statistical indicators, which makes it difficult to extract fault information and thus brings challenges for fault diagnosis. A fault diagnosis method for RV reducer based on stationary condition data capturing is proposed, combined with the motion characteristics of the industrial robot RV reducer in this paper. Firstly, a squeezing area constraint factor is introduced to obtain a clear time-frequency spectrogram. Then, ridge extraction based on fast path optimization is performed and the required stationary condition data is obtained by using the sliding window peak-to-peak and mean indicators. Finally, the fault diagnosis is realized by the envelope spectrum analysis of the stationary condition data. The proposed method is verified by the vibration data from RV reducers under the reciprocating variable speed condition. The experimental results show that this method can capture the stationary condition data accurately, overcome the influence of variable speed conditions and successfully extract the fault information.