Abstract:The diagnosis of composite fault occurred in mechanism is a difficult challenge at present. Due to fault feature of composite failure each other interference, it is hard to composite fault of full and accurate diagnose. This paper presents a diagnosis method that compound fault features separation based on complex network community clustering algorithm. The author firstly do that fault signal is decomposed into several intrinsic mode functions by empirical mode decomposition adaptive. Due to different single fault characteristics will be reflected in different frequency, the author extracts the characteristics of each IMF component and establishes the network model of fault data. We will each intrinsic mode function as the complex network community. According to the characteristics of complex network community structure, we will each intrinsic mode function as the complex network community. Similar community will be merged, which each merge community corresponding different single fault. The last, we will get each community corresponding to different single fault, so as to realize the separation of compound fault. Take the rotor unbalance and bearing inner, bearing inner and bearing roller fault separation characteristics and diagnosis for example, the author proves feasibility of this method.
陈安华 莫志军 蒋玲莉 潘阳 . 基于复杂网络社团聚类的复合故障特征分离诊断方法[J]. 振动与冲击, 2016, 35(7): 76-81.
Chen Anhua, Mo Zhijun,Jiang Lingli,Yang Pan. Compound fault features separation diagnosis method based on complex network community clustering algorithm. JOURNAL OF VIBRATION AND SHOCK, 2016, 35(7): 76-81.
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