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.