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To solve the difficulty in correctly identifying a compound fault of rolling bearing, a method combining variational mode decomposition (VMD) and harmonic fusion vector bispectrum (HFVB) is proposed. Firstly, to achieve adaptive decomposition of signals, the characteristic ability of envelope entropy to represent signal sparsity is utilized. By employing the minimum envelope entropy as the fitness function for the sparrow search algorithm (SSA), the decomposition levels and penalty factors of VMD are adaptively determined. Secondly, root-mean-square value is treated as fault feature index to self-adaptively choose from intrinsic mode function (IMF) which can embody fault features of bearings. Thirdly, to further highlight fault features, HFVB is used to blend chosen IMFs. Finally, faults of bearings were recognized with spectrum of signals. To verify the effectiveness of proposed method, an analysis was given to vibration signals in different situations, and SSA-VMD-HFVB was compared with classical method. The results demonstrate that the proposed SSA-VMD-HFVB method facilitates the adaptive decomposition of VMD, enabling the selection and effective integration of sensitive fault component signals. This approach enhances the accuracy of complex fault diagnosing in rolling bearings. [ABSTRACT FROM AUTHOR]

The paper has brought in the Hjorth Complexity parameter and combined it with Intrinsic time decomposition (ITD) algorithm as characteristic parameter index in order to implement accurate identification of multiple faults of rolling bearings. Firstly, concerning about the much uncertainty in manual setting of decomposition layer number in ITD, overdoing of automatic decomposition and the fact that a larger correlation coefficient of signal relates with the greater correlation of signals before and after decomposition and vice versa, the paper has carried out self-adaptive determination of the number of ITD decomposition layers. Secondly, regarding the insensitivity of Hjorth Complexity parameter to noise and the fact that with larger Complexity parameter, signals are simpler and it becomes more available to dig out characteristic information of fault from signals. With Complexity parameter as the index of characteristic parameter, option of optimal Proper rotation component (PRC) is made after ITD. Finally, through the comparison with other methods and the analysis of multiple faults of bearings, it indicates that correlation coefficient can self-adaptively determine the number of ITD decomposition layers and prevent from overdoing and underdoing of decomposition. The Hjorth Complexity parameter can be treated as index parameter to implement optimal PRC option, based on which multiple fault characteristics of bearings can be effectively extracted and the type precisely determined. • The decomposition layers of ITD are determined based on the correlation coefficient adaptive. • Hjorth parameter is introduced into the feature extraction of rolling bearing compound faults. • Complexity parameter is used as the index parameter of the optimal PRC selection. • Cross-correlation function is brought in to make mixed study on the acceleration signals from different sensors. • ITD is combined with Hjorth parameter. [ABSTRACT FROM AUTHOR]

In view of the difficulty to effectively extract compound faults of rolling bearing from aero-engine and precisely identify their types, the paper has proposed a method integrating signal separation algorithm and information fusion. Firstly, the method decomposes the vibration acceleration signals collected by sensors from different positions at the same moment based on intrinsic time scale decomposition algorithm. Secondly, cross correlation analysis is given to the proper rotation component (PRC) of the same layer, which are obtained after decomposition and correspond to the sensors from different positions and cross-correlation function is introduced to embody information fusion. Thirdly, signals are reconstructed according to cross-correlation function of each PRC. Finally, based on the frequency spectrum of reconstructed signal, extract the characteristics of rolling bearing and identify the type of faults under different sensor combinations and multiple compound fault types. The result shows, the proposed method can effectively extract the characteristics of compound faults of bearing and precisely identify the type of faults under different sensor combinations and multiple compound fault types of rolling bearing. [ABSTRACT FROM AUTHOR]

• Sensibility of variance to variation of signal intensity is used to construct Hankel matrix to highlight fault feature. • Optimal scale of the variance series was determined by sample entropy to create a multi-scale Hankel matrix. • Proposed multiscale singular value method can effectively reduce noise and highlight fault feature information. • Proposed method is insensitive to installation positions of sensors and the fault type of rolling bearings. To solve the vulnerability of singular value denoising to the effective number of singular value orders and influence of Hankel matrix structure, the paper has proposed a multiscale singular value denoising algorithm (M-SVD). Meanwhile, in concerning that when a fault of bearing occurs, the fault feature information contained in vibration signal tends to be weak and complex, the M-SVD method is blended with variational mode decomposition (VMD) algorithm in identification. Firstly, when bearing faults occur, raw vibration acceleration signals are decomposed using the superiority of VMD algorithm for non-stationary signal; in addition, considering that with the larger difference between singular values of signal, feature information will be more obvious, the method takes advantage of sensibility of variance to signal intensity and proposes to constitute Hankel matrix based on variance feature of signal. During the construction of Hankel matrix, the method to determine effective length of variance sequence (optimal scale) has been brought forward by sample entropy which is able to precisely measure the regularity of signal and detect the weak change of signal. Furthermore, component signals are denoised in line with reconstructed Hankel matrix and the spectrum of signals denoised are selected to describe fault characteristic information and judge the failure types. For the sake of verification of the accuracy and effectiveness of proposed method, the paper has analyzed and verified the data of rolling bearing in 3 different failure types and 2 different installation positions of accelerometers. The comparative analysis with classical singular value difference spectrum (SVDS) and singular value median decomposition (SVMD) denoising has further proved that presented VMD combined M-SVD method can effectively restrain noise while retaining fault characteristic information of signal. Therefore, the identification for rolling bearing fault types can be implemented, accurately. [ABSTRACT FROM AUTHOR]

• Intrinsic Time-scale Decomposition and Graph Signal Processing is integrated. • A PR component with minimum Laplacian Energy index is chosen. • The ITD-GSP can precisely provide the characteristic frequency of compound faults. • The ITD-GSP algorithm is insensitive to the installation direction of sensors. The diagnosis of compound faults of rolling bearing is extremely difficult due to the nonlinearity and non-stationary of signal. With the purpose of extracting the compound faults from rolling bearings of aero-engine, efforts were made to integrate Intrinsic Time-scale Decomposition (ITD) and Graph Signal Processing (GSP), namely the ITD-GSP methodology. A PR component (obtained by ITD algorithm) with minimum Laplacian Energy (LE) index is chosen as the optimal rotational component to identify types of compound fault of rolling bearing. The result indicates that the proposed ITD-GSP methodology can precisely and effectively provide the characteristic frequency of compound faults no matter in which direction of sensors (vertical and horizontal) and which type of compound faults of rolling bearing. On the contrary, the mainstream schemes of fault analysis using ITD algorithm (correlation coefficients or kurtosis as the basis to screen optimal rotational component) cannot. [ABSTRACT FROM AUTHOR]