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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]

To properly identify the combined faults of rolling bearings, the paper has proposed a new approach to extract the fault characteristics of rolling bearings based on the combination of discrete wavelet transform (DWT) and generalized S transform (GST). To effectively separate the signals of different frequency bands, original acceleration signal was subjected to wavelet decomposition to obtain approximate and detailed signals. Furthermore, a time–frequency analysis was given to approximate and detailed signals through generalized S transform due to its excellent time–frequency property. At last, according to time–frequency slice diagram of GST, feature frequency of the rolling bearing could be effectively extracted under different combined fault types. The result indicates that the combination of wavelet transform and generalized S transform can identify the combined fault types for bearings more precisely than generalized S transform alone. [ABSTRACT FROM AUTHOR]

To precisely determine the type of bearing fault, the paper has proposed the solution by combining first-order difference of signals and coarse-graining. When a bearing fault occurs, vibration signals are often accompanied by impact features and meanwhile, greater difference among adjacent points of signals corresponds to more evident impact features. Compared with original vibration signals, the first-order difference of signals works better in measuring this kind of impact feature. Therefore, the solution proposed in the paper takes first-order difference in place of original signals and combines with coarse-graining algorithm for subsequent treatment. Compared with average value of signals, the variance of signal can represent the discreteness of signal which describes the impact feature of bearing fault from the other side. Therefore, the solution has coarse-graining operation for difference signals in improved coarse-graining method with variance as criterion to further highlight impact features of rolling bearing. Secondly, a feature set has been established to comprehensively represent fault information of bearing with the average of coarse-graining sequence as feature parameter. Finally, the type of bearing fault is identified according to the fault feature set and support vector machine (SVM). To verify the effectiveness and universality of solution proposed by the paper, a comparison is given to proposed solution and classical method according to dataset of faults of rotor and rolling bearing, and public dataset. The result indicates that with different rotate speeds and types of faults, the proposed solution is more precise to identify the type of bearing faults; in two different datasets, the average identification rate of the proposed solution is 96.43% (aeroengine–rotor–rolling bearing fault dataset) and 100% (public dataset) for unknown samples. [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]