【摘要】 轴承是飞轮和控制力矩陀螺(CMG)等空间惯性执行机构的核心部件,其健康状态直接影响整机性能和使用寿命。当前,由于轻载轴承在正常运转时也可能产生类似于微弱故障特征的现象,导致单一故障特征参数难以辨识正常和微弱故障状态。针对这一问题,本文提出了一种基于振动参数聚类融合的轴承微弱故障辨识方法。首先,通过轴承振动实验获得数据;然后,基于特征频率比值等方法对振动信号进行特征参数的提取;在此基础上,利用K-Medoids算法对正常样本进行聚类,并根据3σ法则构建正常运转的安全边界;最后,计算不同轴承故障数据的超限概率,根据概率大小进行故障状态的识别。结果表明,该方法对轴承正常和微弱故障的辨识是可行和有效的。更多还原

【Abstract】 Bearings are the core components of space inertial actuators such as flywheel and Control Moment Gyros( CMG),and their operating status directly affect the performance and service life of the whole machine. At present,since that light-load bearings may produce a phenomenon like weak fault characteristics during normal operation,it is difficult to identify the normal and weak fault states for a single fault characteristic parameter. Aiming at this problem,this paper proposes a vibration fault clustering fusion method for bearing weak fault identification. First,the data are obtained through the bearing vibration experiment. Then,the characteristic parameters of the vibration signal are extracted based on the characteristic frequency ratio. On this basis,the K-Medoids algorithm is used to cluster normal samples,and the safe boundary of normal operation is constructed according to the 3σ rule. Finally,the overrun probability of different bearing fault data are calculated,and the fault state is identified according to the probability. The results show that this method is feasible and effective for the identification of normal and weak faults of bearings.更多还原