A Fault Diagnosis Scheme for Rolling Bearing Based on Particle Swarm Optimization in Variational Mode Decomposition

Yi, Cancan; Lv, Yong; Dang, Zhang

doi:10.1155/2016/9372691

Cited by 115 publications

(95 citation statements)

References 20 publications

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“…erefore, selecting the suitable initial decomposition parameters has become a critical issue in the study of VMD. Many researches [24][25][26][27][28][29] mainly focused on how to determine the bandwidth parameter and the number of decomposition modes for the fault diagnosis of bearings. However, the current types for initializing the parameters in VMD subject to some prior experience.…”

Section: Introductionmentioning

confidence: 99%

An Enhanced VMD with the Guidance of Envelope Negentropy Spectrum for Bearing Fault Diagnosis

et al. 2020

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Currently, study on the relevant methods of variational mode decomposition (VMD) is mainly focused on the selection of the number of decomposed modes and the bandwidth parameter using various optimization algorithms. Most of these methods utilize the genetic-like algorithms to quantitatively analyze these parameters, which increase the additional initial parameters and inevitably the computational burden due to ignoring the inherent characteristics of the VMD. From the perspective to locate the initial center frequency (ICF) during the VMD decomposition process, we propose an enhanced VMD with the guidance of envelope negentropy spectrum for bearing fault diagnosis, thus effectively avoiding the drawbacks of the current VMD-based algorithms. First, the ICF is coarsely located by envelope negentropy spectrum (ENS) and the fault-related modes are fast extracted by incorporating the ICF into the VMD. Then, the fault-related modes are adaptively optimized by adjusting the bandwidth parameters. Lastly, in order to identify fault-related features, the Hilbert envelope demodulation technique is used to analyze the optimal mode obtained by the proposed method. Analysis results of simulated and experimental data indicate that the proposed method is effective to extract the weak faulty characteristics of bearings and has advantage over some advanced methods. Moreover, a discussion on the extension of the proposed method is put forward to identify multicomponents for broadening its applied scope.

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Section: Introductionmentioning

confidence: 99%

An Enhanced VMD with the Guidance of Envelope Negentropy Spectrum for Bearing Fault Diagnosis

et al. 2020

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“…Due to the EMD experiencing problems of mode aliasing and end effects, Variational Mode Decomposition (VMD) was recently proposed to solve the problem. VMD can solve the problems of EMD [17,20]. The VMD algorithm has been applied to some fields such as in mechanical fault diagnosis, structural damage identification, and it has achieved significant results [21,23].…”

Section: Introductionmentioning

confidence: 99%

Analysis method of impact echo based on variational mode decomposition

Xu¹,

Ren²,

Shen³

2018

J. vibroeng.

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The Impact Echo (IE) method is a widely used method for the detection of concrete structures. The signal has non-stationary characteristics. Empirical mode decomposition (EMD) is one of the typical ways of dealing with non-stationary signals. Due to EMD having mode overlap and a breakpoint effect, Variational Mode Decomposition (VMD) has been proposed to overcome these problems. In this study, VMD is introduced into the analysis of the concrete component detection signals, and the impact echo analysis method based on VMD and marginal spectrum is established. The proposed method to solve the marginal spectrum is used in order to analyze the numerical simulation model's IE signal. This method is able to correctly obtain the main frequency. When compared with the traditional Fourier spectrum, the marginal spectrum that is based on the VMD method shows that the band width that is present in the main frequency is narrow. Even in the presence of a highly noisy background, the method is able to correctly obtain the main frequency. At last, through the detection signal analysis of the non-defective concrete specimen and the concrete specimen with the circular hole, the results show that the proposed method can distinguish the signal in the different defective states of the concrete structures, which has obvious advantages compared with the Fourier spectrum.

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“…There is no doubt that fault diagnosis technology, based on vibration signal processing, is critical for monitoring the health of key structures or equipment [1][2][3][4][5]. Generally, different sensors are utilized to obtain mechanical vibration information, from which the features of the running state are characterized [6][7][8].…”

Section: Introductionmentioning

confidence: 99%

Research on the Blind Source Separation Method Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD and Its Application in Diagnosing Rolling-Bearing Faults

Xiao

et al. 2017

Applied Sciences

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Abstract:To improve the performance of single-channel, multi-fault blind source separation (BSS), a novel method based on regenerated phase-shifted sinusoid-assisted empirical mode decomposition (RPSEMD) is proposed in this paper. The RPSEMD method is used to decompose the original single-channel vibration signal into several intrinsic mode functions (IMFs), with the obtained IMFs and original signal together forming a new observed signal for the dimensional lifting. Therefore, an undetermined problem is transformed into a positive definite problem. Compared with the existing EMD method and its improved version, the proposed RPSEMD method performs better in solving the mode mixing problem (MMP) by employing sinusoid-assisted technology. Meanwhile, it can also reduce the computational load and reconstruction errors. The number of source signals is estimated by adopting singular value decomposition (SVD) and Bayes information criterion (BIC). Simulation analysis has demonstrated the superiority of this method being applied in multi-fault BSS. Furthermore, its effectiveness in identifying the multi-fault features of rolling-bearing has been also verified based on a test rig.

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A Fault Diagnosis Scheme for Rolling Bearing Based on Particle Swarm Optimization in Variational Mode Decomposition

Cited by 115 publications

References 20 publications

An Enhanced VMD with the Guidance of Envelope Negentropy Spectrum for Bearing Fault Diagnosis

An Enhanced VMD with the Guidance of Envelope Negentropy Spectrum for Bearing Fault Diagnosis

Analysis method of impact echo based on variational mode decomposition

Research on the Blind Source Separation Method Based on Regenerated Phase-Shifted Sinusoid-Assisted EMD and Its Application in Diagnosing Rolling-Bearing Faults

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