盲解卷积和频域压缩感知在轴承复合故障声学诊断的应用

针对时域盲解卷积算法对单一故障机械声信号有效,及传统稀疏分量分析对声信号分析失效等问题,提出一种盲解卷积、形态滤波和频域压缩感知重构的稀疏分量分析相结合的轴承复合故障声学诊断方法。通过时域盲解卷积算法优选分量结果,提取声信号的冲击成分。使用形态滤波滤除背景噪声。使用模糊C均值聚类估计混合矩阵,重构传感矩阵,并运用稀疏度自适应匹配追踪基算法(Sparsity adaptive matching pursuit,SAMP)的频域压缩感知重构分离信号。双通道滚动轴承故障声信号分析结果表明该方法能够有效分离和提取滚动轴承故障特征。

Blind Deconvolution and Frequency Domain Compressive Sensing Application in Bearing Composite Acoustic Fault Diagnosis

According to the problem of time-domain blind deconvolution algorithm is active only for a single fault mechanical sound signal, and the traditional sparse component analysis is failure to the acoustic signal analysis, a method based on blind deconvolution, morphological filtering and frequency domain compressed sensing reconstruction of sparse component analysis is proposed to deal with the composite acoustics bearing fault diagnosis. The time-domain blind deconvolution algorithm is used to prefer solution components result as well as to extract the impact component of the acoustic signal. Background noise is filtered out by using the morphological filtering. By using fuzzy C-means clustering estimated mixing matrix, the sensor matrix is remodeled based on mixing matrix, and the sparsity adaptive matching pursuit based algorithm of frequency-domain compressed sensing algorithm is used to reconstruct the separated signals. Dual real rolling bearing fault acoustic signal analysis results show that this method can effectively separate and extract the rolling bearing fault characteristics.