Fault Diagnosis and Analysis of Ball Mill Jars Based on Wavelet Denoising and AR model Power Spectrum

Abstract

Ball mill safety and stability is of great significance in industrial production. As a typical case of the signals with multiple mixed similar vibration forms and meshing noise, ball mill jars (BMJ), is selected to diagnosis and analysis in detail. The traditional method of fault diagnosis of gear mesh noise has high requirements on the machine operating environment, and is insensitive to the characteristics of the acoustic signal that is not tightly fitted. A new method, the wavelet denoising and combined with power spectrum, is proposed for the signal that BMJ and base are not tightly fit to produce a piecewise polynomial structure acoustic signal. The acoustic vibration signal is denoised by wavelet, and then the filtered signal is processed by the AR model power spectrum. By analyzing the characteristic power spectrum image, the unfit state can be diagnosed with high accuracy. In order to verify the effectiveness of the method, it is compared with the processed power spectrum image after the noise signal is processed. Experimental results show that the method has better diagnostic performance. The method is an automatic fault diagnosis and early warning system with high precision and robustness in noisy environments.