基于GMM均值超向量稀疏分解的碰摩声发射源识别

Rubbing Acoustic Emission Recognition Based on GMM Mean Supervector Sparse Decomposition

研究提出了一种基于GMM均值超向量稀疏分解的风电机组碰摩声发射源识别检测方法。以压缩感知理论与GMM识别模型为基础,将高斯分量的均值向量连接组成GMM均值超向量作为压缩感知的稀疏基,并利用MP、OMP算法进行稀疏分解,根据稀疏系数识别风电机组碰摩声发射源,而不需要对信号进行重建,提高了识别性能和效率。通过实验观察上位机输出结果,验证了识别结果数据的实时性和准确性,达到了对风电机组运行的状态信息监测和故障诊断的要求。

A new method for the identification and detection of the acoustic emission source of the wind turbine generator based on the sparse decomposition of the GMM mean is proposed.Based on compressed sensing theory and GMM model,the Gauss mean vectors are used to generate GMM mean supervectors,which are sparse basis of compressed sensing theory.Then,the sparse decomposition is done based on MP and OMP algorithm.According to the sparse coefficient,the wind turbine rubbing acoustic emission source is identified without the signal reconstruction.This method improves the recognition performance and efficiency.The experimental results verifies the real-time performance and accuracy.And,the algorithm can meet the requirements of the state information monitoring and fault diagnosis of wind turbine operation.