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基于运动模式划分的工业机器人健康监测方法

Health Monitoring Method of Industrial Robots Based on Motion Pattern Division

  • 摘要: 由于工业机器人存在多运动模式耦合的问题,传统的健康监测方案需要在每个关节处单独安装传感器,难以满足实际工业现场需求。本文以6关节工业机器人为研究对象,基于振动信号研究了多运动模式切换场景下工业机器人的健康监测方法。首先,通过跳变点算法实现机器人运动模式划分,获取不同运动模式对应的信号区间。其次,对不同运动模式的信号分别提取监测指标。最后,基于控制图法实现工业机器人不同关节的健康监测。在工业机器人退化实验数据中验证了本文所提方法,表明本文所提方法能够在仅使用两个振动传感器的条件下实现机器人6个关节的健康状态监测。本文所提运动模式划分算法在对大量历史退化数据进行分析时,所需运行时间更短、单次精度更高且重复性更好。本文提出的方法能够在使用少量传感器的条件下,有效避免运动模式耦合和采样信号的差异可能导致的监测结果误判,使得监测结果更加精准可靠,适用于实际工业现场的工业机器人健康状态监测。

     

    Abstract: Due to the problem of multi-motion mode coupling of industrial robots,the traditional health monitoring scheme needs to install sensors at each joint separately,which is difficult to meet the needs of actual industrial field.A 6-joints industrial robot is taken as the research object,and the health monitoring method of industrial robot in multi-motion mode switching scenario is studied based on vibration signals.Firstly,robot motion modes are divided by jump point algorithm,and signal intervals corresponding to different motion modes are obtained.Secondly,the monitoring indexes are extracted from the signals of different motion modes.Finally,the health monitoring of different joints of industrial robots is realized based on the control chart method.The proposed method is verified by the experimental data of industrial robot degradation.It shows that the proposed method can realize the health status monitoring of 6 joints of the robot with only two vibration sensors.The motion pattern partitioning algorithm proposed in this paper costs shorter running time,has higher single precision and better repeatability when analyzing a large number of historical degraded data.The proposed method can effectively avoid the misjudgment of monitoring results which may be caused by the difference of motion mode coupling and sampling signal under the condition using only a few sensors,making the monitoring results more accurate and reliable,and more suitable for the health status monitoring of industrial robots in actual industrial sites.

     

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