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为了更有效、直观地对航空发动机的振动状态进行实时监控,运用信息熵和模糊支持向量机(FSVM)方法,建立了基于信息熵距和FSVM隶属度的转子振动状态评估方法。研究了振动信号的信息熵特征,提出了可以表示转子振动状态的指标—信息熵距;通过模糊支持向量机(FSVM)确定模糊隶属度矩阵,将模糊隶属度矩阵与信息熵距相结合,建立了一个多参数的转子振动状态评估模型;应用此模型对转子振动信号进行系统分析和定量计算,验证了该方法用于转子振动状态评估是有效、可行的。
In order to monitor the vibration state of aeroengine more effectively and intuitively, the method of rotor vibration state evaluation based on information entropy and FSVM membership is established by using information entropy and fuzzy support vector machine (FSVM) method. The characteristic of information entropy of the vibration signal is studied. The index of information entropy which can represent the vibration state of the rotor is proposed. The fuzzy membership matrix is determined by fuzzy support vector machine (FSVM). The fuzzy membership degree matrix is combined with the information entropy distance A multi-parameter rotor vibration state evaluation model is proposed. By using this model, the rotor vibration signals are systematically analyzed and quantified. The results show that the proposed method is effective and feasible for rotor vibration assessment.