To timely detect the incipient failure of rolling bearing and find out the accurate fault location, a novel rolling bearing fault diagnosis method is proposed based on the composite multiscale fuzzy ...entropy (CMFE) and ensemble support vector machines (ESVMs). Fuzzy entropy (FuzzyEn), as an improvement of sample entropy (SampEn), is a new nonlinear method for measuring the complexity of time series. Since FuzzyEn (or SampEn) in single scale can not reflect the complexity effectively, multiscale fuzzy entropy (MFE) is developed by defining the FuzzyEns of coarse-grained time series, which represents the system dynamics in different scales. However, the MFE values will be affected by the data length, especially when the data are not long enough. By combining information of multiple coarse-grained time series in the same scale, the CMFE algorithm is proposed in this paper to enhance MFE, as well as FuzzyEn. Compared with MFE, with the increasing of scale factor, CMFE obtains much more stable and consistent values for a short-term time series. In this paper CMFE is employed to measure the complexity of vibration signals of rolling bearings and is applied to extract the nonlinear features hidden in the vibration signals. Also the physically meanings of CMFE being suitable for rolling bearing fault diagnosis are explored. Based on these, to fulfill an automatic fault diagnosis, the ensemble SVMs based multi-classifier is constructed for the intelligent classification of fault features. Finally, the proposed fault diagnosis method of rolling bearing is applied to experimental data analysis and the results indicate that the proposed method could effectively distinguish different fault categories and severities of rolling bearings.
•Composite multiscale fuzzy entropy is proposed for measuring the complexity of time series.•CMFE is compared with multiscale fuzzy entropy.•CMFE is employed to extract the hidden features from vibration signals.•A novel fault diagnosis method is proposed based on the CMFE and ensemble SVMs.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
A typical symptom of vibration signals collected from rolling bearings with local faults is the existence of periodic transients, which makes the intrinsic structure of vibration signals become more ...and more regular. Generally, the vibration always contains multiple intrinsic oscillatory modes on different scales, which generally is caused by the interaction and coupling of machine components. Therefore, it is necessary to detect the behavior change of complexity of vibration signals in the view of multiple scales for fault information representation. The complexity and nonlinear failure symptom of rolling bearing can be evaluated by the recently proposed nonlinear dynamic tools, such as multiscale entropy (MSE) and its variants. Recently, the improved MSE method, multiscale dispersion entropy (MDE) and its improvement refined composite MDE (RCMDE) are developed to measure the complexity of time domain data. However, the intrinsic shortages of coarse graining approach that used in MDE and RCMDE have limited their application to fault feature representation. In this paper, an improved RCMDE approach named generalized refined composite multiscale fluctuation-based fractional dispersion entropy (GRCMFDE) is proposed to enhance MDE and RCMDE in complexity measurement of time series. GRCMFDE was compared with MPE, MDE, RCMDE by analyzing synthetic simulation signals to verify its advantages. After that, an intelligent fault diagnosis method was proposed by combining GRCMFDE with supervised multi-clustering feature selection and gray wolf optimized SVM for fault classification of rolling bearing. Lastly, the proposed fault diagnostic method was applied to two experimental data set analysis by comparing with multiscale permutation entropy, MDE- and RCMDE-based fault diagnostic methods and the comparison results indicate that the proposed method can effectively diagnose the fault locations and severities of rolling bearing and get a higher fault identifying rate than the comparative methods.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OBVAL, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
•SGMD has good decomposition performance in dealing with complex signals.•SGMD, using symplectic geometry similarity transformation, can keep the essential character of the original signals ...unchanged.•The SGMD method is applied to the noisy signal to verify its robustness.•The SGMD performs better on the diagnosis of rotating machinery compound fault.
Various existed time-series decomposition methods, including wavelet transform, ensemble empirical mode decomposition (EEMD), local characteristic-scale decomposition (LCD), singular spectrum analysis (SSA), etc., have some defects for nonlinear system signal analysis. When the signal is more complex, especially noisy signal, the component signal is forced to decompose into several incomplete components by LCD and SSA. In addition, the wavelet transform and EEMD need user-defined parameters, and they are very sensitive to the parameters. Therefore, a new signal decomposition algorithm, symplectic geometry mode decomposition (SGMD), is proposed in this paper to decompose a time series into a set of independent mode components. SGMD uses the symplectic geometry similarity transformation to solve the eigenvalues of the Hamiltonian matrix and reconstruct the single component signals with its corresponding eigenvectors. Meanwhile, SGMD can efficiently reconstruct the existed modes and remove the noise without any user-defined parameters. The essence of this method is that signal decomposition is converted into symplectic geometry transformation problem, and the signal is decomposed into a set of symplectic geometry components (SGCs). The analysis results of simulation signals and experimental signals indicate that the proposed time-series decomposition approach can decompose the analyzed signals accurately and effectively.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•A nonlinear sparse mode decomposition (NSMD) is proposed.•The proposed NSMD method can avoid modal aliasing and improve the orthogonality of decomposition.•NSMD method has good robustness and ...adaptability.
Traditional time-frequency analysis methods, including empirical mode decomposition (EMD), local characteristic-scale decomposition (LCD) and variable mode decomposition (VMD), have some limitations in nonlinear signal analysis. When the signal has strong noise, traditional time-frequency analysis methods will force the signal to be decomposed into several inaccurate components, and the achieved components usually suffer from the end effect problem. Considering the above pressing challenge, a new signal decomposition algorithm, nonlinear sparse mode decomposition (NSMD), is proposed in this protocol. The core of NSMD is that the local narrowband signal disappears under the action of the singular local linear operator, so the singular local linear operator can be applied to extract the local narrowband component of the detected signal. Meanwhile, the obtained local narrowband signal can be superposed as the basic signal to close to the original signal, realizing the adaptive decomposition of the signal with good robustness and adaptability. The analysis results of simulation signals and planetary gearbox fault signals indicate that the proposed NSMD method is effective for raw vibration signals.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
•An improved signal processing method, called ISGMD, is proposed.•The short time series can be obtained by adding a slip window.•Variable entropy is proposed to measure the amount of fault feature ...information.•The experimental results show that the ISGMD is effective for early gear fault diagnosis.
Symplectic geometry mode decomposition (SGMD) is an effective signal processing method, and it has been applied in compound fault diagnosis successfully. However, for early gear fault vibration signals, SGMD has two shortcomings. On the one hand, SGMD directly reconstructs the trajectory matrix through the original time series, which may cause the weak fault features submerged in global time series. Therefore, add a slip window to preprocess the original time series. On the other hand, the symplectic geometry components (SGCs) with low energy and fault feature information are eliminated for denoise. Therefore, variable entropy (VE) weighting is proposed to obtain the weighted symplectic geometry components (WSGCs) containing the vast majority of fault feature information. In conclusion, an improved symplectic geometry mode decomposition (ISGMD) is proposed to overcome the above two shortcomings. Simulated and experimental results indicate that ISGMD is effective for raw vibration signals.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
Inspired by the empirical wavelet transform method, a newly nonstationary signal analysis method–termed order-statistic filtering Fourier decomposition is proposed in this article. First, ...order-statistic filtering Fourier decomposition uses order-statistic filtering and smoothing to preprocess the Fourier spectrum of original signal, which resolves the problem of unreasonable boundaries obtained by empirical wavelet transform in segmenting the Fourier spectrum. Then, the mono-components with physical significance are obtained by adaptively reconstructing the coefficient of fast Fourier transform in each interval, which improves the problem of too many false components obtained by Fourier decomposition method. The order-statistic filtering Fourier decomposition method is compared with the existing nonstationary signal decomposition methods including empirical mode decomposition, empirical wavelet transform, Fourier decomposition method, and variational mode decomposition through analyzing simulation signals, and the result indicates that order-statistic filtering Fourier decomposition is much more accurate and reasonable in obtaining mono-components. After that, the order-statistic filtering Fourier decomposition method is compared with the mentioned methods in diagnostic accuracy through analyzing the tested faulty bearing vibration signals and the effectiveness of order-statistic filtering Fourier decomposition to the comparative methods in bearing fault identification are verified.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
•The MEAutogram method is proposed to enhance the performance of Autogram.•The superiority of MEAutogram to Autogram and fast kurtogram is verified.•The SGMD and MEAutogram based fault diagnosis ...method for gear is proposed.•The effectiveness and superiority of our proposed method are verified by comparing with existing methods.
Autogram is an effective optimal frequency band selection method, in which the signal spectrum is divided by the maximum overlapping discrete wavelet packet transform (MODWPT) and the position of maximum kurtosis value is used as the optimal frequency band. However, Autogram follows a binary tree structure in segmenting frequency domain and its segmentation position is fixed, this causes that its position cannot be adaptively determined according to the signal characteristics. To solve this issue, in this paper an improved frequency band selection method called maximum envelope based-Autogram (MEAutogram) is proposed. In MEAutogram method, the maximum value envelope method is used to process the signal spectrum and then the minimum value point closest to the middle position of adjacent maximum value points in envelope signal is used as the segmentation position. However, the segmentation accuracy of MEAutogram will decrease when the signal contains lots of irrelevant components. The recently proposed nonlinear time series analysis method termed symplectic geometry mode decomposition (SGMD) founded on the symplectic matrix similar transformation is used to remove irrelevant components. Based on this, a new SGMD and MEAutogram based fault diagnosis method for gear is proposed. The proposed fault diagnosis method of gear can reduce the calculation amount through overcoming the influence of irrelevant components on the segmentation position, which can be adaptively determined according to the characteristics of the raw signal. Finally, the analysis results of simulation and gear test data was used to verify that the appropriate demodulation frequency band can be accurately detected by the proposed method and the fault characteristics obtained by the proposed method are more obvious than that of the comparative methods.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UILJ, UL, UM, UPCLJ, UPUK, ZAGLJ, ZRSKP
The strip filling mining method can solve the problems related to mining under structures, under aquifers, and under infrastructure (3U coal seams), while the reasonable selection of the mining and ...filling sequence still requires further investigation. The current study was conducted to investigate the stress and surface subsidence of a filling body (or coal pillar) under two filling sequences through theoretical analysis, similar simulation tests, and numerical simulations. To achieve optimal filling materials for the Liudong Coal Mine, a low‐strength similar paste filling material composed of fly ash, gypsum, and sand was developed. The relationships between the strength and the cement ratio and between the strength and the sand‐binder ratio were discussed. Similar simulation tests showed that mining scheme 1 (mining before filling) could lead to the formation of an isolated island coal pillar in the mining process, mining scheme 2 (filling before mining) had less influence on surface subsidence, and the stress on the filling body was smaller for scheme 2 than for scheme 1. The distributions of the stress and plastic zone in the two different mining and filling sequences were obtained through numerical simulations. The method of first filling and then mining could greatly reduce the stress concentration and plastic zone during the mining process. In summary, mining scheme 2 (filling before mining) can avoid the formation of isolated island coal pillars in the process of mining, and without considering other factors, scheme 2 should be adopted as much as possible.
Mining scheme 1 (mining before filling) could lead to the formation of an isolated island coal pillar in the mining process, mining scheme 2 (filling before mining) had less influence on surface subsidence, and the stress on the filling body was smaller for scheme 2 than for scheme 1. The method of first filling and then mining could greatly reduce the stress concentration and plastic zone during the mining process. In summary, mining scheme 2 (filling before mining) can avoid the formation of isolated island coal pillars in the process of mining, and without considering other factors, scheme 2 should be adopted as much as possible.
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FZAB, GIS, IJS, KILJ, NLZOH, NUK, OILJ, SAZU, SBCE, SBMB, UL, UM, UPUK
Machinery fault diagnosis tasks have been well addressed when sufficient and abundant data are available. However, the data imbalance problem widely exists in real-world scenarios, which leads to the ...performance deterioration of fault diagnosis markedly. To solve this problem, we present a novel imbalanced fault diagnosis method based on the enhanced generative adversarial networks (GAN). By artificially generating fake samples, the proposed method can mitigate the loss caused by the lack of real fault data. Specifically, in order to improve the quality of generated samples, a new discriminator is designed using spectrum normalization (SN) strategy and a two time-scale update rule (TTUR) method is used to stabilize the training process of GAN. Then, an enhanced Wasserstein GAN with gradient penalty is developed to generate high-quality synthetic samples for the fault samples set. Finally, a deep convolutional classifier is constructed to carry out fault classification. The performance and effectiveness of the proposed method are validated on the Case Western Reserve University bearing dataset and rolling bearing dataset acquired from our laboratory. The simulation results show that the proposed method has a superior performance than other methods for imbalanced fault diagnosis tasks.
Recently, it has been theoretically predicted that Cd3As2 is a three dimensional Dirac material, a new topological phase discovered after topological insulators, which exhibits a linear energy ...dispersion in the bulk with massless Dirac fermions. Here, we report on the low-temperature magnetoresistance measurements on a ~50 nm-thick Cd3As2 film. The weak antilocalization under perpendicular magnetic field is discussed based on the two-dimensional Hikami-Larkin-Nagaoka (HLN) theory. The electron-electron interaction is addressed as the source of the dephasing based on the temperature-dependent scaling behavior. The weak antilocalization can be also observed while the magnetic field is parallel to the electric field due to the strong interaction between the different conductance channels in this quasi-two-dimensional film.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK
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