Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system

Wang, Yanxue; Markert, Richard; Xiang, Jiawei; Zheng, Weiguang

doi:10.1016/j.ymssp.2015.02.020

Cited by 468 publications

(222 citation statements)

References 16 publications

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“…For example, Yao et al [18] utilised variational mode decomposition and robust independent component analysis to separate the noise source of diesel engine. Moreover, these methods are widely used in bearing fault diagnosis [19][20][21].…”

Section: Introductionmentioning

confidence: 99%

Radiation Noise Separation of Internal Combustion Engine Based on Gammatone-RobustICA Method

Yao

Xiang

Qian

et al. 2017

Shock and Vibration

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In the internal combustion engine noise source separation process, the combustion noise and the piston slap noise are found to be seriously aliased in time-frequency domain. It is difficult to accurately separate them. Therefore, the noise source separation method which is based on Gammatone filter bank and robust independent component analysis (RobustICA) is proposed. The 6-cylinder internal combustion engine vibration and noise test are carried out in a semianechoic chamber. The lead covering method is adopted to isolate the interference noise from numbers 1 to 5 cylinder parts, with only the number 6 cylinder parts left bare. Firstly, many mode components of the measured near-field radiated noise signals are extracted through the designed Gammatone filter bank. Then, the RobustICA algorithm is utilised to extract the independent components. Finally, the spectrum analysis, the continuous wavelet time-frequency analysis, the correlation function method, and the drag test are employed to further identify the separation results. The research results show that the frequency of the combustion noise and the piston slap noise are, respectively, concentrated at 4025 Hz and 1725 Hz. Compared with the EWT-RobustICA method, the separation results obtained by the Gammatone-RobustICA method have very fewer interference components.

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Section: Introductionmentioning

confidence: 99%

Radiation Noise Separation of Internal Combustion Engine Based on Gammatone-RobustICA Method

Yao

Xiang

Qian

et al. 2017

Shock and Vibration

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“…Variational mode decomposition (VMD) is a newly developed technique for adaptive signal decomposition, and can non-recursively decompose a multi-component signal into a number of quasi-orthogonal intrinsic mode functions. Wang et al [205] proposed a novel method for the rub-impact fault diagnosis of the rotor system based on VMD, and proved that multiple features can be better extracted with the VMD than empirical WT (EWT), EEMD, and EMD.…”

Section: Emd Lmd and Vmdmentioning

confidence: 99%

Basic research on machinery fault diagnostics: Past, present, and future trends

et al. 2017

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Machinery fault diagnosis has progressed over the past decades with the evolution of machineries in terms of complexity and scale. High-value machineries require condition monitoring and fault diagnosis to guarantee their designed functions and performance throughout their lifetime. Research on machinery Fault diagnostics has grown rapidly in recent years. This paper attempts to summarize and review the recent R&D trends in the basic research field of machinery fault diagnosis in terms of four main aspects: Fault mechanism, sensor technique and signal acquisition, signal processing, and intelligent diagnostics. The review discusses the special contributions of Chinese scholars to machinery fault diagnostics. On the basis of the review of basic theory of machinery fault diagnosis and its practical applications in engineering, the paper concludes with a brief discussion on the future trends and challenges in machinery fault diagnosis.

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“…[4][5][6] The signal decomposition process is transferred to the variational model, and the adaptive decomposition of the signal is realized by searching the optimal solution of the constrained variational model. The frequency center and bandwidth of each component are constantly updated in the iterative solution of the variational mode.…”

Section: Variational Mode Decompositionmentioning

confidence: 99%

Seismic Signal De-Noising Using Variational Mode Decomposition and Wavelet Transform

Xue¹

2017

IPCSE

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Seismic denoising is a key step in seismic signal processing for enhancing the resolution of the seismic signal. In this paper, a seismic denoising method is introduced by using variational mode decomposition (VMD) combined with wavelet transform. VMD is used to decompose the seismic data into a series of intrisic mode functions (IMFs) from low frequency to high frequency. Since the noise is always concentrated in the high frequency components, IMFs with high frequency are denoised by using wavelet transform with a soft threshold to effectively remove the noise. Finally, the denoised IMFs and other remained IMFs are reconstructed to form a denoised signal. Synthetic data and real data are used to validate the method.

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Research on variational mode decomposition and its application in detecting rub-impact fault of the rotor system

Cited by 468 publications

References 16 publications

Radiation Noise Separation of Internal Combustion Engine Based on Gammatone-RobustICA Method

Radiation Noise Separation of Internal Combustion Engine Based on Gammatone-RobustICA Method

Basic research on machinery fault diagnostics: Past, present, and future trends

Seismic Signal De-Noising Using Variational Mode Decomposition and Wavelet Transform

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