摘要
传统的诊断系统中,对旋转机械轴心轨迹通常采用人工诊断方法,极大地影响诊断的自动化程度,为此,提出了一种基于模糊小波神经网络的旋转机械轴心轨迹识别方法,该方法将模糊分类方法与小波神经网络相结合用于轴心轨迹的识别,将模糊隶属度函数作为输入和输出。通过仿真实验对该方法进行验证,并对实验结果进行了分析,结果表明轴心轨迹的形状特征与频率特征并不是一一对应的,同时指出该方法识别结果与实际情况吻合,具有较高的识别能力。
The axle center orbit of the rotary machine is used to be diagnosed artificially. To improve the automatization of the diagnosis, an axle center orbit identification algorithm based on fuzzy wavelet networks is put forward. This method combines the fuzzy classification method with the wavelet network for identification of the axle center orbit, and takes the functions of fuzzy membership degree as input and output. The method is verified by simulation experiment, and the result of the experiment is analyzed. It is indicated that this method has high identification capability.
出处
《石油机械》
北大核心
2005年第6期14-16,i002,共4页
China Petroleum Machinery
基金
黑龙江省教委科研基金资助项目
项目名称:基于模糊综合分析识别转动设备几种故障的方法研究(10541007)
