Fault diagnosis of rolling bearing based on a mine fan bearing

Abstract

Rolling bearings are the most common and easily damaged link in mine fans, and there are many problems that can be improved in the acquisition and analysis of vibration signals. Through the establishment of a mining-based rotating machinery failure test platform, the bearing is subjected to fault simulation experiments and the collected signals are processed, and the collected bearing signals are compared without noise reduction processing and the use of noise reduction fusion spectrum algorithm and wavelet noise reduction method After noise reduction, the analysis results are processed by Hilbert transform and EMD (empirical modal analysis), and the frequency domain diagram obtained by comparing the frequency domain map is compared to observe the frequency of abnormal vibration to further infer the fault type. By comparing the analysis results obtained by different methods, they are compared and summarized, so as to promote the upgrading and improvement of processing methods, make accurate judgments on fan bearing faults and give feasibility opinions.