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SIMULATION CIRCUIT FAULT DIAGNOSIS METHOD BASED ON CONTINUOUS WAVELET ANALYSIS AND ELM NETWORK

机译:基于连续小波分析和ELM网络的仿真电路故障诊断方法

摘要

A simulation circuit fault diagnosis method based on continuous wavelet analysis and an ELM network, comprising: data acquisition: performing data sampling on output responses of a simulation circuit by means of Multisim simulation to acquire an output response data set; characteristic extraction: carrying out continuous wavelet analysis by taking the output response data set of the circuit as a training and test data set to acquire a wavelet time-frequency coefficient matrix, the coefficient matrix being segmented into eight sub-matrices of a same size, and performing singular value decomposition on the sub-matrices to calculate a Tsallis entropy of each sub matrix, so as to form a corresponding fault characteristic vector; and fault classification: submitting the characteristic vectors of all the samples to the ELM network so as to realize accurate and rapid fault classification. The method has good characteristic extraction effect on circuit faults, and can implement accurate and efficient classification of the circuit faults.
机译:一种基于连续小波分析和ELM网络的仿真电路故障诊断方法,包括:数据采集:利用Multisim仿真对仿真电路的输出响应进行数据采样,获取输出响应数据集;特征提取:通过将电路的输出响应数据集作为训练和测试数据集进行连续小波分析,以获取小波时频系数矩阵,该系数矩阵被分割为相同大小的八个子矩阵,对子矩阵进行奇异值分解,计算出每个子矩阵的Tsallis熵,以形成对应的故障特征矢量。故障分类:将所有样本的特征向量提交给ELM网络,以实现准确,快速的故障分类。该方法对电路故障具有良好的特征提取效果,可以对电路故障进行准确,高效的分类。

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