Research On The Health Status Assessment Of Rolling Bearings Based On Improved DLMD

Rolling bearing is one of the most important and easily damaged key parts of rotating machinery.It often runs in high-speed,heavy load and complex working conditions.Its health will directly affect the working state of the whole machinery and equipment,which may cause economic losses,and may cause casualties in serious cases.The performance degradation of rolling bearing is a gradual process.If we know its fault evolution law and further evaluate its health status,we can provide guidance for its reliability maintenance.Therefore,it is of great practical significance to effectively evaluate the health status of rolling bearing during operation.At present,there are many nonlinear signal analysis methods for rolling bearing vibration signal,such as Local mean decomposition(LMD),Differential local mean decomposition(DLMD)and so on,but there are some problems such as false frequency and differential frequency selection.At the same time,building a simple and effective method to characterize the performance of rolling bearing and evaluate the health status of rolling bearing is the basis and core for maintenance personnel to make maintenance plan.However,there are some problems,such as missing information caused by single characteristic parameter and lack of reliable health assessment model.In conclusion,this paper focuses on the improvement of dlmd performance and the construction of appropriate health assessment model,the main research work is as follows:1.For the lack of theoretical guidance or solutions in the selection of differential times in dlmd,this paper proposes a Hilbert differential local mean decomposition(HDLMD)method based on Hilbert demodulation.Firstly,the vibration signal of rolling bearing is decomposed by LMD,and multiple Product functions(PF)components are obtained.Then,the Hilbert method is used to demodulate all PF components,and then the frequency aliasing of PF components is identified according to Hilbert spectrum.If there is frequency aliasing,the signal after the first differentiation will continue to be differentiated until there is no frequency aliasing phenomenon after the LMD decomposition of the signal after the k-th differentiation.Finally,the differentiation degree of dlmd is k,which improves the dlmd differentiation degree Number selection method.Experimental results show that there is no false component in hdlmd decomposition and no mode aliasing in power spectrum,which proves the effectiveness of the proposed method.2.Based on the sum of the distance between the midpoint and the local mean and the absolute skewness,an adaptive method of Crt-differential local mean decomposition(Crt-DLMD)is proposed,which overcomes the subjective limitation of intuitively observing the number of differentiations based on Hilbert spectrum.Firstly,the Crt based differentiation index is constructed to optimize the differentiation degree of dlmd and obtain several pf components.Secondly,the sensitivity factor is constructed by adjusting pf components based on Probability distribution function(PDF)and linear interpolation.Finally,The TKEO spectrum of the reconstructed signal is demodulated and analyzed by using the Teager kaisernergy operator,and the TKEO spectrum is calculated to realize the fault feature extraction of rolling bearing.According to the tkeo spectrum of the experimental results and SP calculation time,it can be seen that Crt-DLMD decomposition results are more accurate and faster.3.At present,the process of multi-scale entropy coarsening will lead to the problems of strong individuality and unobvious universality in the results of fluctuation and health status assessment model.A rolling bearing health state assessment method based on Improved cross fuzzy entropy(ICFE)and Weibull proportional hazards model(WPHM)is proposed.Firstly,the original vibration signal is decomposed by Crt-DLMD,and the effective component containing the most fault information is selected by SP for reconstruction;then,the moving mean is used for coarse granulation,and the ICFE of the reconstructed signal is calculated;finally,the ICFE is used as the covariate of wphm for health assessment.The experimental results show that the proposed method is reliable.