改进频域盲反卷积算法在轴承声故障特征提取中的应用

Lifeng Kan, Nan Pan, Zeguang Yi
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引用次数: 1

摘要

本文采用基于频域盲反卷积的声学故障诊断方法,从麦克风采集的混合声信号中分离出不同来源的误差特征信号。采用滑动窗口短时傅里叶变换(STFT)将时域卷积混合模型转化为瞬时频域混合模型,并采用改进的复不动点算法对同频复信号进行盲分离处理。通过计算Kullback-Leibler (KL)距离来解决盲源分离过程中的阶数不确定性,然后利用小波分析对分离信号细节进行重构,得到最终的分离信号。最后,通过对计算机仿真信号的分析和滚动轴承试验台的实验,验证了该算法的有效性。
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Improved frequency domain blind deconvolution algorithm in acoustic fault feature extraction of bearing
In this paper, acoustic fault diagnosis method based on frequency domain blind deconvolution was used to separate different sources of error characteristic signal from the mixed acoustic signal collected in microphone. Sliding window short time Fourier transform (STFT) is used to convert time domain convolution mixture model into a instantaneous frequency domain mixture model, and Improved complex fixed-point algorithms is used for the blind separation process of complex signals of the same frequency. Kullback-Leibler (KL) distance is calculated to solve order uncertainty from the blind source separation process, then wavelet analysis is used to reconstructed separated signal details to get the final split signals. Finally, by analyzing the computer simulative signal and experiments for the test rig for rolling bearing, the effectiveness of the algorithm can be verified.
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