Method Research Of Fault Diagnose Of Rolling Bearing Based On EEMD And Sliding Kurtosis Demodulation

Rolling bearing is one of the most widely used components of rotating machinery,and its healthy condition play an important role in the reliability and production efficiency of the entire mechanical equipment.The working environment of the rolling bearing is complex,and its early fault features are weak and easily submerged by strong background noise,which makes it difficult to extract fault features.Therefore,the methods of noise reduction and fault feature extraction from early weak fault signals of rolling bearings are deeply studied in this paper.The main research work is as follows:(1)In order to reduce the noise of early weak fault signals,a noise reduction method based on adaptive EEMD is proposed.Firstly,an EEMD adaptive parameter optimization method with uniform extreme points is proposed to optimize the white noise amplitude and the overall average number of times,which can improve the decomposition accuracy and computational efficiency.Then,a fuzzy comprehensive decision criterion based on correlation kurtosis and information entropy is proposed to optimize IMF components for reconstruction.The results show that the method is better than the kurtosis method and the correlation coefficient method in the optimization of IMF components.(2)To resolve the problem of early fault feature extraction,a method for rolling bearing based on improved sliding kurtosis demodulation is proposed.Firstly,the sliding window width is determined with the minimum information entropy.Then,the sliding kurtosis algorithm is used to calculate the sliding kurtosis time series to realize the kurtosis envelope,which solves the envelope problem and extracts the fault characteristics of the weak shock signal.Adaptive EEMD and sliding kurtosis algorithm are combined to give full play to their own characteristics and improve diagnostic accuracy.The validity of the method is verified by simulation signal and test signal analysis.(3)Based on the NI platform,the new method presented in this paper is developed as an early bearing fault diagnosis module.Through the verification of a large number of tests,the feasibility and effectiveness of the system are verified.