The fault feature signal of rolling bearing can be characterized as the narrow-band signal with a specific resonance frequency. Therefore, resonance demodulation analysis is a powerful damage detection technique of bearings. In addition to the fault feature signal, the measured vibration signals carry various interference components, and these interference components become a serious obstacle of fault feature extraction. Variational mode extraction is a novel signal analysis method designed to retrieve a specific signal component from the composite signal. Variational mode extraction is founded on a similar basis as variational mode decomposition, while it shows better accuracy and higher efficiency compared with variational mode decomposition. In this study, variational mode extraction is introduced to the resonance demodulation analysis of bearing fault. As the results of variational mode extraction analysis are greatly influenced by the choice of two parameters, that is, the balancing factor α and the initial guess of center frequency ωd, an optimized variational mode extraction method is further developed. First, a new fault information evaluation index for measuring the richness of fault characteristics of the signal, termed ensemble impulsiveness and cyclostationarity, is formulated. Second, the ensemble impulsiveness and cyclostationarity is used as the fitness function of particle swarm optimization to automatically determine the optimal values of α and ωd. Finally, the validity of optimized variational mode extraction method is verified by simulated and experimental analysis, and the superiority of optimized variational mode extraction method is highlighted through comparison with two other advanced resonance demodulation analysis approaches, that is, the improved kurtogram and infogram. The analysis results indicate that optimized variational mode extraction method has a powerful capability of resonance demodulation analysis.
Synchrosqueezed wave packet transform (SSWPT) can effectively reconstruct the band-limited components of the signal by inputting the specific reconstructed boundaries and it provides an alternative bearing fault diagnosis method. However, the selection of reconstructed boundaries can significantly affect the fault feature extraction performance of SSWPT. Accordingly, this paper presents a boundary division guiding SSWPT (BD-SSWPT) method. In this method, an adaptive boundary division method is developed to effectively determine the reconstructed boundaries of SSWPT. Firstly, the marginal spectrum of SSWPT, more robust to noise than the Fourier spectrum, is defined for the scale-space division to obtain the initial boundaries. Secondly, the inverse transform of SSWPT is conducted based on the initial boundaries to obtain the initial reconstructed components. Thirdly, a boundary redefinition scheme, composed of clustering and combination, is conducted to redefine the boundaries. Finally, the potential components are extracted by the inverse transform of SSWPT based on the redefined boundaries. The validity of BD-SSWPT is verified by simulated and experimental analysis, and the superiority of BD-SSWPT is highlighted through comparison with singular spectrum decomposition (SSD) and an adaptive parameter optimized variational mode decomposition (AVMD). The results demonstrate that BD-SSWPT identifies more significant fault features and has higher computational efficiency than SSD and AVMD.
Variational mode extraction (VME), inspired by variational mode decomposition (VMD), is a novel fault diagnosis technique that can efficiently extract narrowband modes from multi-component signals. Compared with VMD, VME is more accurate and faster when extracting the narrowband component. However, the preset center frequency ω c and balance factor α seriously affect the performance of VME. Therefore, a spectral-coherence guided variational mode extraction (SCVME), capable of determining the hyper-parameters automatically, is proposed for fault diagnosis of rolling bearings. First, considering the advantages of spectral coherence (SCoh) for characterizing the cyclostationarity of bearing faults, its energy spectrum is further constructed. The energy spectrum of SCoh can intuitively reveal the fault information energy hidden in each frequency, which provides sufficient support for the determination of the center frequency ω c . Subsequently, a novel signal evaluation index named cyclic pulse intensity (CPI) is proposed to adaptively optimize the balance factor α. It has been verified that the proposed CPI index is superior to these common metrics, including kurtosis, spectral kurtosis (SK) and l2/l1 norm, in identifying periodic pulses. Finally, the modes containing fault information are accurately extracted by VME according to the optimal parameters (ω c , α). The effectiveness of the proposed method has been demonstrated by the simulations and experiments analysis. In addition, comparisons with the VMD and Autogram methods are carried out to highlight the superiority of the SCVME method.
The core of fault diagnosis of rolling bearing is to extract the narrowband sub-components containing fault feature information from the bearing fault signal. Variational mode extraction (VME), a novel single sub-component separation algorithm originated from variational mode decomposition (VMD), provides a promising solution to bearing fault detection. However, its performance is closely related to the hyperparameter selection, including the center frequency ω d and the penalty factor α. This paper proposes a non-recursive and adaptive signal decomposition algorithm termed spectral variational mode extraction (SVME). SVME can be seen as a spectral decomposition technique whose framework is composed of the adaptive spectral boundary division and boundary constrained VME. In the adaptive spectral boundary division, an adaptive iterative spectral envelope method referring to the continuous envelope correlation (CCE) index is developed to integrate with the parameterless scale-space division to adaptively locate the frequency band boundary. The presented adaptive spectral boundary division approach can effectively inhibit the spectral boundary over-division. In the boundary constrained VME, the dominant frequency of each frequency band determined by the optimal spectral division is distinguished as the preset center frequency. Meanwhile, the optimal penalty factor is determined based on the envelope spectral kurtosis (ESK) index and the boundary-constraint principle. The SVME method is utilized in the simulation and experimental case studies to investigate its capability. Furthermore, its superiority is highlighted through the comparison with the variational mode decomposition (VMD) and Autogram methods.
scite is a Brooklyn-based organization that helps researchers better discover and understand research articles through Smart Citations–citations that display the context of the citation and describe whether the article provides supporting or contrasting evidence. scite is used by students and researchers from around the world and is funded in part by the National Science Foundation and the National Institute on Drug Abuse of the National Institutes of Health.
hi@scite.ai
10624 S. Eastern Ave., Ste. A-614
Henderson, NV 89052, USA
Copyright © 2024 scite LLC. All rights reserved.
Made with 💙 for researchers
Part of the Research Solutions Family.