Multichannel Signal Denoising Using Multivariate Variational Mode Decomposition With Subspace Projection

Cao, Peipei; Wang, Huali; Zhou, Kaijie

doi:10.1109/access.2020.2988552

Cited by 30 publications

(4 citation statements)

References 19 publications

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“…As a widely used multichannel decomposition technique for fault information separation, MVMD adaptively decomposes the signal into several IMFs, thereby separating fault transients from interference and noise. In this study, the decomposition level was set to 5 and the other parameters are initialized with fixed values empirically according to [33]. Figures 14 and 15 present the resulting 5 IMFs and their respective spectra in two channels.…”

Section: Case 1: Locomotive Bearing With Inner Race Faultmentioning

confidence: 99%

Tensor low-rank and sparse decomposition and its application in bearing fault information separation

Ou,

Zhao,

et al. 2024

Meas. Sci. Technol.

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Properly separating fault information from noisy measured signals is crucial for effective bearing health sensing. However, conventional fault information separation methods face challenges such as predefined model parameters and poor noise robustness. Additionally, with the advent of Industry Big Data, multichannel monitoring signals present significant challenges for traditional single decomposition approaches. To address these challenges and fully extract potential fault information, this paper introduces a tensor low-rank and sparse decomposition (tensor LRSD) approach for multichannel signal processing. Inspired by matrix LRSD, we construct a tensor LRSD model that adaptively decomposes the signal into a tensor sparse term containing fault information and a low-rank term representing the intrinsic signal pattern. To further enhance the decomposition performance, a maximum correlation-based selection strategy is designed. This strategy evaluates the correlation between each tensor slice and selects appropriate tensor sparse terms for fault information extraction. Simulation analysis and two experimental studies involving typical bearing failures are implemented to verify the capability and superiority of the presented tensor LRSD approach. The consequences demonstrate that the presented method outperforms conventional techniques, showcasing its capability to effectively separate fault information from noisy signals.

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Section: Case 1: Locomotive Bearing With Inner Race Faultmentioning

confidence: 99%

Tensor low-rank and sparse decomposition and its application in bearing fault information separation

Ou,

Zhao,

et al. 2024

Meas. Sci. Technol.

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“…Similarly, the VMD methods employ an optimization framework to separate the complex signal into multiple modes. To deal with multivariate data, the multivariate extension of EMD (i.e., multivariate EMD, or MEMD for short) and VMD (i.e., multivariate VMD, or MVMD for short) are proposed for processing multivariate data to obtain the IMFs with aligned frequency ranges [32][33][34][35]. Noise-assisted MEMD (NA-MEMD) [33], partial noise-assisted MEMD (PNA-MEMD) [36], and sinusoidal signal-assisted MEMD (SA-MEMD) [37] and harmonic-assisted MEMD (HA-MEMD) [38] are subsequently proposed to improve the performance of the MEMD method by adding additional channels with independent white noise, highfrequency band-limited noise, and a sinusoidal assisted signal, respectively.…”

Section: Introductionmentioning

confidence: 99%

Adaptive noise suppression for low-S/N microseismic data based on ambient-noise-assisted multivariate empirical mode decomposition

Huang

Fang

et al. 2023

Front. Phys.

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Microseismic monitoring data may be seriously contaminated by complex and nonstationary interference noises produced by mechanical vibration, which significantly impact the data quality and subsequent data-processing procedure. One challenge in microseismic data processing is separating weak seismic signals from varying noisy data. To address this issue, we proposed an ambient-noise-assisted multivariate empirical mode decomposition (ANA-MEMD) method for adaptively suppressing noise in low signal-to-noise (S/N) microseismic data. In the proposed method, a new multi-channel record is produced by combining the noisy microseismic signal with preceding ambient noises. The multi-channel record is then decomposed using multivariate empirical mode decomposition (MEMD) into multivariate intrinsic mode functions (MIMFs). Then, the MIMFs corresponding to the main ambient noises can be identified by calculating and sorting energy percentage in descending order. Finally, the IMFs associated with strong interference noise, high-frequency and low-frequency noise are filtered out and suppressed by the energy percentage and frequency range. We investigate the feasibility and reliability of the proposed method using both synthetic data and field data. The results demonstrate that the proposed method can mitigate the mode mixing problem and clarify the main noise contributors by adding additional ambient-noise-assisted channels, hence separating the microseismic signal and ambient noise effectively and enhancing the S/Ns of microseismic signals.

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“…Therefore, MVMD as an extension of VMD also has a parameter optimization problem. Although the multiple signal input activates the noise reduction capability of the Wiener filter and reduces the effect of the number of IMF K on the decomposition effect [ 14 ], the iterative optimization-seeking process of MVMD converges too slowly, and the decomposition effect is still affected by the penalty factor α.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Rolling Mill Multi Row Bearings Based on AMVMD-MC1DCNN under Unbalanced Dataset

Zhao

Sun

Lin

et al. 2021

Sensors

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Rolling mill multi-row bearings are subjected to axial loads, which cause damage of rolling elements and cages, so the axial vibration signal contains rich fault character information. The vertical shock caused by the failure is weakened because multiple rows of bearings are subjected to radial forces together. Considering the special characters of rolling mill bearing vibration signals, a fault diagnosis method combining Adaptive Multivariate Variational Mode Decomposition (AMVMD) and Multi-channel One-dimensional Convolution Neural Network (MC1DCNN) is proposed to improve the diagnosis accuracy. Additionally, Deep Convolutional Generative Adversarial Network (DCGAN) is embedded in models to solve the problem of fault data scarcity. DCGAN is used to generate AMVMD reconstruction data to supplement the unbalanced dataset, and the MC1DCNN model is trained by the dataset to diagnose the real data. The proposed method is compared with a variety of diagnostic models, and the experimental results show that the method can effectively improve the diagnosis accuracy of rolling mill multi-row bearing under unbalanced dataset conditions. It is an important guide to the current problem of insufficient data and low diagnosis accuracy faced in the fault diagnosis of multi-row bearings such as rolling mills.

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Multichannel Signal Denoising Using Multivariate Variational Mode Decomposition With Subspace Projection

Cited by 30 publications

References 19 publications

Tensor low-rank and sparse decomposition and its application in bearing fault information separation

Tensor low-rank and sparse decomposition and its application in bearing fault information separation

Adaptive noise suppression for low-S/N microseismic data based on ambient-noise-assisted multivariate empirical mode decomposition

Fault Diagnosis Method for Rolling Mill Multi Row Bearings Based on AMVMD-MC1DCNN under Unbalanced Dataset

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