Degradation Feature Extraction And Performance Degradation Assessment Of Rolling Bearings

Rotating machinery is widely used in the industrial field,and rolling bearing is the key component which commonly used in rotating machinery.Bearing failures may lead to cascading failures of mechanical systems,economic losses and even catastrophic accident.Therefore,the health condition of bearings has a significant impact on the reliability,availability,maintainability and safety of the whole machine.Accurate completion of performance degradation assessment of rolling bearings is conducive to timely detection of bearing degradation in practical engineering and formulation of targeted maintenance plans,so as to improve maintenance efficiency.One of the main methods to realize bearing performance degradation state assessment is based on the analysis of vibration signals.However,it is difficult to directly evaluate the degradation state of bearings due to the rich information of the data actually collected,which is nonlinear and non-stationary.Therefore,this paper carries out research from three aspects: signal preprocessing,degradation characteristics and performance degradation assessment.The main research contents are as follows:(1)In view of the traditional adaptive decomposition method defects modal aliasing,endpoint effect,this article through the variational mode decomposition(VMD)method for signal preprocessing,and through the fusion of correlation coefficient,dynamic time neat and kurtosis is worth to the integrated index,according to the integrated index selection modal component containing fault information is most complete signal reconstruction,for subsequent analysis.The method is applied to the actual rolling bearing signal analysis,and the experimental results show that the method can complete the signal preprocessing better,and realize the signal decomposition and denoising.(2)In view of the problem that information loss and falsification of multiscale dispersion entropy(MDE)can easily occur in the process of coarse grain,and it is difficult to fully extract bearing fault information,improved refined composite multiscale normalized dispersion entropy(IRCMNDE)is proposed as the degenerative feature of bearings.First,the refined composite multiscale dispersion entropy(RCMDE)is introduced,and the RCMDE is improved to represent the maximum value of data segment information in the coarsening process to overcome the shortcomings of the traditional coarsening process and highlight the fault characteristics.Then,IRCMNDE is obtained by reducing the entropy fluctuation caused by different parameter choices during entropy calculation through normalization operation.Finally,the actual bearing signals were analyzed,and the experimental results showed that IRCMNDE effectively overcame the defects of MDE in the coarse-granulation process,and was able to extract the fault feature information of rolling bearings more accurately than MDE,RCMDE and IRCMDE.(3)To solve the problem that most existing methods only rely on amplitude similarity but ignore angle similarity,a performance degradation assessment method based on cosine Euclidean distance is proposed.First,the cosine Euclidean distance is obtained by combining the cosine distance with the Euclidean distance.Then,taking IRCNMDE as the degradation feature,the cosine Euclidean distance between the health data and the test data IRCNMDE entropy value was calculated,and the degradation trend curve of rolling bearings was drawn.Finally,Chebyshev inequality was used to establish the bearing health threshold and evaluate the degradation state of bearing performance.The experimental results show that the cosine Euclidean distance can effectively and timely judge the performance degradation state of rolling bearings,and can better describe the performance degradation trend of rolling bearings.