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Mode Identification Based on Fuzzy Clustering and Grey System Theory and its Application

机译:基于模糊聚类和灰色系统理论及其应用的模式识别

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The health condition of rotor has been greatly concerned in rotating machinery. But for the lack of information, it is very difficult to judge the actual condition. Based on the fuzzy and grey characteristics between faults and symptoms, a new method integrated with fuzzy clustering and grey relation analysis was put forward to identify the condition of rotor system. Firstly, eight features, such as average value, peak-peak value, variance value, virtual value and etc., were extracted from the vibration signal of rotor system. Then, fuzzy C-means algorithm was used to cluster forty samples into 4 clusters, meanwhile, the clustering center was acquired and regarded as standard pattern matrix. Finally, the grey relation degree was calculated between pattern to be inspected and the standard pattern matrix. Using this method, the unbalanced conditions of rotor system was precisely identified, which shows that the integrated method is valid and practicable.
机译:转子的健康状况在旋转机械方面得到了极大的关注。但对于缺乏信息,很难判断实际情况。基于故障与症状之间的模糊和灰色特性,提出了一种与模糊聚类和灰色关系分析集成的新方法,以确定转子系统的条件。首先,从转子系统的振动信号中提取八个特征,例如平均值,峰值值,方差值,虚拟值等。然后,模糊C-Means算法用于将四十个样本集聚到4个集群中,同时获取聚类中心并被视为标准模式矩阵。最后,计算待检查模式的灰色关系度和标准图案矩阵。使用该方法,精确地识别了转子系统的不平衡条件,这表明集成方法是有效和切实可行的。

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