Research On Machinery Fault Diagnosis Based On Variational Mode Decomposition

Rotor and bearing are important parts of many mechanical equipment in industrial production machinery,which have an important influence on the normal operation of the machine.Fault vibration signals are usually non-stationary,nonlinear,non-Gaussian and so on.Single method is difficult to extract the effective fault vibration signal features in the raw signal.This article adopts the new signal processing method Variational Mode Decomposition to be combined with other methods of signal processing to analyze the mechanical fault vibration signal.The main research contents are as follows:1.Rotor fault time-frequency analysis method based on VMDA signal processing method was proposed for rotor fault diagnosis problem based on VMD.The center frequency and bandwidth of each component were determined in the process of the obtainment of the components by iteratively searching the optimal solution of variational model.Thus the signal frequency domain was adaptively subdivided and effectively separated from each component.The signal instantaneous frequency and amplitude of each single component could be obtained by Hilbert transform.To verify the effectiveness of the proposed method,a comparison was made to show the performance of VMD and EMD in the analysis of simulation signal and some typical rotor fault signals.The decomposition results of simulation signal show that the VMD can accurately decompose the intrinsic modes and have no modal mixing.Rotor fault experiment signal analysis results show that the proposed method can effectively extract the evident fault features,so as to accurately diagnose the rotor fault.2.Rolling bearing fault feature extraction based on VMD and 1.5-dimensional Teager energy spectrumCombine VMD with 1.5-dimensional Teager energy spectrum,the purpose of rolling bearing fault diagnosis can be achieved.Fault feature extraction process are as follows: Firstly,the rolling bearing fault signal was decomposed using VMD.The two components,which had obvious impact features,were extracted and reconstructed using the kurtosis-correlation coefficient criteria.Secondly,the reconstructed signal was analyzed using the 1.5-dimensional Teager energy spectrum.According to the energy spectrum analysis of the reconstructed signal,the inner ring and rolling element fault features were extracted.The analysis of the simulation signal and the test signal verifies the effectiveness of the proposed method.Compared with ensemble empirical mode decomposition,the proposed method would be more distinctive and effectively identify fault feature of the rolling bearing.3 Rolling bearing fault diagnosis based on VMD,fuzzy entropy and fuzzy C-means clusteringA method for mechanical fault diagnosis based on VMD,fuzzy entropy and fuzzy C-means clustering(FCM)was proposed.Firstly,fault signal was decomposed by VMD,the components of higher mutual information value to make up the initial feature vector matrix.The fuzzy entropy values of initial feature vector matrix were calculated to make up the feature vector matrix.Finally,input the fuzzy entropy values feature vector matrix to the FCM to diagnosis the fault mode.The method is applied to recognize the rolling bearing fault pattern.Compared with the method based on EMD and FCM,it can be effectively applied to rolling bearing fault diagnosis.