Fault Diagnosis of Hydraulic Pumps Using PSO-VMD and Refined Composite Multiscale Fluctuation Dispersion Entropy

Zhou, Fuming; Yang, Xiaoqiang; Shen, Jinxing; Liu, Wuqiang

doi:10.1155/2020/8840676

Cited by 35 publications

(21 citation statements)

References 35 publications

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“…Along with the emergence of intelligent optimization algorithms, attempts have been made to apply some optimization algorithms in the optimization of VMD parameters, and satisfying results have been obtained [24,29,30]. Zhou et al [31] put forward the particle swarm optimization (PSO) to optimize the VMD parameters. In this method, they used mean permutation entropy (MPE) as its fitness function and determined the optimal combination of K and α by searching for the minimum of the fitness function.…”

Section: Introductionmentioning

confidence: 99%

A Vibration Signal Denoising Method of Marine Atomic Gravimeter Based on Improved Variational Mode Decomposition

Gong

Liao

et al. 2022

Wireless Communications and Mobile Computing

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As a high-precision gravity measuring device, a marine atomic gravimeter is highly sensitive to vibration signals. Accurate measurement and analysis of vibration signal is the primary condition to realize vibration compensation and vibration suppression. Denoising plays a crucial role in the processing of these vibration signals. The vibration signals of a marine gravimeter contain numerous nonlinear and nonstationary components. In this paper, a vibration signal denoising method of marine atomic gravimeter based on improved variational mode decomposition (VMD) was put forward to effectively suppress the noise. An improved genetic particle swarm optimization (GPSO) was first adopted for the parametric optimization of VMD by taking minimum permutation entropy (PE) as fitness function and adaptively determining the optimal parameters of VMD. PE was then utilized to calculate the proportion of noise-containing components in the intrinsic mode function (IMF) components obtained by VMD. The components were classified into noise and signal components by searching for the mutation points of two adjacent IMF permutation entropies. On this basis, noise components were denoised by Savitzky-Golay (SG) filter. In the end, the denoised components were reconstructed with the signal components to generate denoised vibration signals. To verify the effectiveness, the proposed method was applied in denoising, simulated and measured vibration signals of a marine atomic gravimeter, and compared with Daubechies (db) wavelet, Symlets (sym) wavelet, and empirical mode decomposition (EMD). The results showed that the proposed method could effectively remove the noise from nonlinear vibration signals and retain the authentic and useful information, so that it was able to provide the supporting data for gravity compensation of marine atomic gravimeter.

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Section: Introductionmentioning

confidence: 99%

A Vibration Signal Denoising Method of Marine Atomic Gravimeter Based on Improved Variational Mode Decomposition

Gong

Liao

et al. 2022

Wireless Communications and Mobile Computing

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“…To conquer the shortcomings of the coarsening method in the multi-channel signals for MFDE, On the basis of MFDE, GAN et al introduced the coarse-grained method and proposed the composite multi-scale wave dispersion entropy (CMFDE) [ 14 ], proving that the sliding coarse-grained method based on CMFDE has better entropy stability. Zhou et al proposed that refined complex multi-scale fluctuation dispersion entropy (RCMFDE) [ 15 ] is stronger and more stable in extracting features, and RCMDE has a smaller dependence on the length of time series. Azami et al proposed multi-variable multi-scale dispersion entropy (MMDE) [ 16 ] in order to quantify the complexity of multivariate time series.…”

Section: Introductionmentioning

confidence: 99%

A Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy and Its Application to Multivariate Signal of Rotating Machinery

Yang

Jiang

et al. 2021

Entropy

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In the fault monitoring of rotating machinery, the vibration signal of the bearing and gear in a complex operating environment has poor stationarity and high noise. How to accurately and efficiently identify various fault categories is a major challenge in rotary fault diagnosis. Most of the existing methods only analyze the single channel vibration signal and do not comprehensively consider the multi-channel vibration signal. Therefore, this paper presents Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy (RCMMFDE), a method which extracts the recognition information of multi-channel signals with different scale factors, and the refined composite analysis ensures the recognition stability. The simulation results show that this method has the characteristics of low sensitivity to signal length and strong anti-noise ability. At the same time, combined with Joint Mutual Information Maximisation (JMIM) and support vector machine (SVM), RCMMFDE-JMIM-SVM fault diagnosis method has been proposed. This method uses RCMMFDE to extract the state characteristics of the multiple vibration signals of the rotary machine, and then uses the JMIM method to extract the sensitive characteristics. Finally, different states of the rotary machine are classified by SVM. The validity of the method is verified by the composite gear fault data set and bearing fault data set. The diagnostic accuracy of the method is 99.25% and 100.00%. The experimental results show that RCMMFDE-JMIM-SVM can effectively recognize multiple signals.

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“…But in wavelet transform, the choice of wavelet basis has great influence on the result, and there is no adaptive to the local analysis of the signal. Through the research of many scholars, it is found that the most effective way to separate the useful signal from the noisy signal is to apply the idea of signal decomposition [9]- [11], but it is still not easy to extract the weak signal features under the strong noise background, and there are three main difficulties in this regard:  The application scope of the current weak signal feature extraction methods is relatively limited, so it is difficult to find a method that is simultaneously suitable for detecting the signal under nonlinear, unstable and non-Gaussian noise environment.…”

Section: Introductionmentioning

confidence: 99%

Composite Multi-Scale Basic Scale Entropy Based on CEEMDAN and Its Application in Hydraulic Pump Fault Diagnosis

et al. 2021

Self Cite

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The hydraulic pump plays a very important role in the safe and stable operation of the hydraulic system. Once it fails, it will cause immeasurable losses to the entire hydraulic system. But in practice, because hydraulic pump often works under strong noise background, the fault characteristics of its vibration signals are often very weak and difficult to extract. To solve this problem, this paper proposes an effective time series dynamic feature extraction method, which is based on the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and composite multi-scale basic scale entropy (CMBSE). On this basis, a new hydraulic pump fault diagnosis method is proposed by combining tdistributed stochastic neighbor embedding (t-SNE) and whale optimization algorithm kernel extreme learning machine (WOA-KELM). First, CEEMDAN is used to decompose the fault signals of the hydraulic pump, and CMBSE is used to quantify the decomposed IMF components to obtain the fault characteristics of the different states of the hydraulic pump, and then use t-SNE to visualize the dimensionality reduction, and finally input into the WOA-KELM-based fault classifier for state identification. The experimental results show that this method can effectively extract the weak signal features under strong noise background, and has broad application prospects for hydraulic pump fault diagnosis.

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Fault Diagnosis of Hydraulic Pumps Using PSO-VMD and Refined Composite Multiscale Fluctuation Dispersion Entropy

Cited by 35 publications

References 35 publications

A Vibration Signal Denoising Method of Marine Atomic Gravimeter Based on Improved Variational Mode Decomposition

A Vibration Signal Denoising Method of Marine Atomic Gravimeter Based on Improved Variational Mode Decomposition

A Refined Composite Multivariate Multiscale Fluctuation Dispersion Entropy and Its Application to Multivariate Signal of Rotating Machinery

Composite Multi-Scale Basic Scale Entropy Based on CEEMDAN and Its Application in Hydraulic Pump Fault Diagnosis

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