Wu Dengyun, Wang Jianwen, Wang Hong, Liu Hongxing, Lai Lin, He Tian, Xie Tao
State Key Laboratory of Robotics and System, Harbin Institute of Technology, Harbin 150001, China.
Science and Technology on Space Intelligent Control Laboratory, Beijing Key Laboratory of Long-life Technology of Precise Rotation and Transmission Mechanisms, Beijing Institute of Control Engineering, Beijing 100194, China.
Sensors (Basel). 2020 Mar 10;20(5):1519. doi: 10.3390/s20051519.
Bearing is a key component of satellite inertia actuators such as moment wheel assemblies (MWAs) and control moment gyros (CMGs), and its operating state is directly related to the performance and service life of satellites. However, because of the complexity of the vibration frequency components of satellite bearing assemblies and the small loading, normal running bearings normally present similar fault characteristics in long-term ground life experiments, which makes it difficult to judge the bearing fault status. This paper proposes an automatic fault diagnosis method for bearings based on a presented indicator called the characteristic frequency ratio. First, the vibration signals of various MWAs were picked up by the bearing vibration test. Then, the improved ensemble empirical mode decomposition (EEMD) method was introduced to demodulate the envelope of the bearing signals, and the fault characteristic frequencies of the vibration signals were acquired. Based on this, the characteristic frequency ratio for fault identification was defined, and a method for determining the threshold of fault judgment was further proposed. Finally, an automatic diagnosis process was proposed and verified by using different bearing fault data. The results show that the presented method is feasible and effective for automatic monitoring and diagnosis of bearing faults.
轴承是诸如动量轮组件（MWA）和控制力矩陀螺（CMG）等卫星惯性执行机构的关键部件，其运行状态直接关系到卫星的性能和使用寿命。然而，由于卫星轴承组件振动频率成分的复杂性以及载荷较小，正常运行的轴承在长期地面寿命试验中通常呈现相似的故障特征，这使得难以判断轴承的故障状态。本文基于一种称为特征频率比的指标，提出了一种轴承自动故障诊断方法。首先，通过轴承振动试验采集各种MWA的振动信号。然后，引入改进的总体经验模态分解（EEMD）方法对轴承信号的包络进行解调，获取振动信号的故障特征频率。在此基础上，定义了用于故障识别的特征频率比，并进一步提出了确定故障判断阈值的方法。最后，提出了一种自动诊断流程，并通过使用不同的轴承故障数据进行了验证。结果表明，所提出的方法对于轴承故障的自动监测和诊断是可行且有效的。
