| Rolling bearings as one of the key components of machinery and equipment,any minor bearing failure can affect the operational status of the entire system.To ensure the reliable operation of equipment,it is necessary to effectively identify the operating status of rolling bearings.Bearing vibration signals are often coupled by multiple component vibration modes and noise,and traditional signal processing methods do not provide satisfactory results for complex signal.Symplectic Geometry Mode Decomposition(SGMD)has the advantage of keeping the essential features of the time series unchanged,suppressing mode confusion and effectively removing noise in the process of reconstructing a single component.Based on an in-depth study of the SGMD theory,this paper focuses on the extraction of rolling bearing fault features and fault degree evaluation of rolling bearings under the background of noise.The main research contents are as follows:1.Aiming at the problem that SGMD reorganizes symplectic geometry components by calculating the component correlation of the initial components,the standard is ambiguous,the calculation is large,and the theoretical basis is lacking.A quantity law that the eigenvalues of the Hamilton matrix are twice the number of signal frequencies is found,and a theoretical derivation is given.A feature extraction method based on SGMD’s eigenvalue quantity law is proposed.Firstly,the vibration signal trajectory matrix is constructed to perform symplectic geometry similarity transformation to obtain the Hamilton matrix’s eigenvalue distribution spectrum;then the diagonal averaging is performed to obtain initial symplectic geometry components of the vibration signal;Finally,the initial symplectic geometry components are reorganized according to the magnitude and order of the frequency amplitude to extract the corresponding frequency components.Simulation and experimental analysis results show that this method can accurately separate harmonic signals and extract the fundamental frequency of rolling bearings,with small phase lag,better matching of feature waveforms and better noise reduction capabilities.2.Aiming at the problem that fault impact features of rolling bearing are affected by background noise and other interference,it is difficult to extract bearing fault features and the extracted fault features are not clear,a rolling bearing fault feature extraction method based on cepstrum pre-whitening and SGMD’s eigenvalue quantity law is proposed.Firstly,cepstrum editing pre-whitening is used for noise reduction.Then envelope spectrum analysis is carried out to initially extract fault features.Finally,according to SGMD’s eigenvalue quantity law,SGMD decomposition components are reconstructed and Hilbert envelope spectrum analysis is carried out to extract fault feature frequencies.Simulation and experimental analysis show that the proposed method can effectively eliminate the influence of background noise and interference frequency components,and the calculation efficiency and extraction effect are better than wavelet-SVD.3.Aiming at the problem that the early fault features of rolling bearings are weak and easily disturbed by background noise and other components,which makes it difficult to detect quantitatively and causes misjudgement of the fault degree,a rolling bearing fault degree evaluation method based on SGMD-MOMEDA and Lempel-Ziv is proposed.Firstly,the bearing vibration signal is decomposed by SGMD,the optimal SGC component is selected according to the maximum kurtosis criterion.Then MOMEDA is used to extract the periodic fault impact component and Lempel-Ziv is calculated,and the bearing fault degree is evaluated according to its change rule.Finally,the Lempel-Ziv value interval for different fault degrees is given based on " 6σ principle".Simulation and experiment show that this method can effectively suppress the interference of noise on indicators,and improve the accuracy and effectiveness of the evaluation of bearing failure. |
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