GMPSO-VMD Algorithm and Its Application to Rolling Bearing Fault Feature Extraction

Ding, Jiakai; Huang, Liangpei; Xiao, Dongming; Li, Xuejun

doi:10.3390/s20071946

Cited by 50 publications

(38 citation statements)

References 45 publications

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“…Therefore, it is necessary to set the appropriate α and K through the optimization algorithm. In this paper, the selection range of α is [50, 3500], and the selection range of K is [2,12].…”

Section: Multi-objective Multi-island Genetic Algorithm Optimizes the Parameters Of Vmdmentioning

confidence: 99%

“…Figure 12 shows the change process of fitness values of signals under four rotational speed conditions with the increase of iterations in the optimization process. Finally, the VMD optimal parameter combinations [K 0 , α 0 ] of signals under four rotational speed conditions are found as [11,350], [10,3236], [11,750], [12,501]. According to the optimal parameter combination [K 0 , α 0 ], the VMD parameters of signals under four rotational speed conditions are set and the signal is decomposed by VMD.…”

Section: 45mentioning

confidence: 99%

“…However, the decomposition result of VMD largely depends on the choice of penalty factor

and decomposition number

. Wang et al [ 12 ] proposed an improved VMD method based on the beetle antenna search (BAS) algorithm, which takes the kurtosis value of the intrinsic mode function as the fitness function in the search process, so as to optimize the parameters of the VMD algorithm. Gai et al [ 13 ] optimized the parameters of VMD by the hybrid Gray Wolf algorithm and applied it to the extraction of bearing fault features.…”

Section: Introductionmentioning

confidence: 99%

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Application of Parameter Optimized Variational Mode Decomposition Method in Fault Feature Extraction of Rolling Bearing

Liang

Sun

2021

Entropy

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The decomposition effect of variational mode decomposition (VMD) mainly depends on the choice of decomposition number K and penalty factor α. For the selection of two parameters, the empirical method and single objective optimization method are usually used, but the aforementioned methods often have limitations and cannot achieve the optimal effects. Therefore, a multi-objective multi-island genetic algorithm (MIGA) is proposed to optimize the parameters of VMD and apply it to feature extraction of bearing fault. First, the envelope entropy (Ee) can reflect the sparsity of the signal, and Renyi entropy (Re) can reflect the energy aggregation degree of the time-frequency distribution of the signal. Therefore, Ee and Re are selected as fitness functions, and the optimal solution of VMD parameters is obtained by the MIGA algorithm. Second, the improved VMD algorithm is used to decompose the bearing fault signal, and then two intrinsic mode functions (IMF) with the most fault information are selected by improved kurtosis and Holder coefficient for reconstruction. Finally, the envelope spectrum of the reconstructed signal is analyzed. The analysis of comparative experiments shows that the feature extraction method can extract bearing fault features more accurately, and the fault diagnosis model based on this method has higher accuracy.

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Section: Multi-objective Multi-island Genetic Algorithm Optimizes the Parameters Of Vmdmentioning

confidence: 99%

Section: 45mentioning

confidence: 99%

“…However, the decomposition result of VMD largely depends on the choice of penalty factor

and decomposition number

Section: Introductionmentioning

confidence: 99%

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Application of Parameter Optimized Variational Mode Decomposition Method in Fault Feature Extraction of Rolling Bearing

Liang

Sun

2021

Entropy

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“…Meanwhile, to optimally select the parameter K of VMD, the genetic variation sample group, kurtosis criterion variational mode decomposition, and self-organizing mapping (SOM) neural network have been adopted to adaptively determine the optimal value of the parameter K in [9][10][11][12]. To verify the effectiveness of these new methods, some experiment examples have been used to simulate in [13][14][15][16][17]. e simulated results showed that these improved models may solve the shortcomings to a certain extent.…”

Section: Introductionmentioning

confidence: 99%

A Classification Algorithm of Fault Modes-Integrated LSSVM and PSO with Parameters’ Optimization of VMD

Huang

Qin

2021

Mathematical Problems in Engineering

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To overcome the shortcomings that the early fault characteristics of rolling bearing are not easy to be extracted and the identification accuracy is not high enough, a novel collaborative diagnosis method is presented combined with VMD and LSSVM for incipient faults of rolling bearing. First, the basic concept of VMD was introduced in detail, and then, the adaptive selection principle of parameter K in VMD was constructed by instantaneous frequency mean. Furthermore, we used Lagrangian polynomial and Euclidean norm to verify the value of K accurately. Secondly, we proposed a classification algorithm based on PSO-optimized LSSVM. Meanwhile, the flowchart of the classification algorithm of fault modes may be also designed. Third, the experiment shows that the presented algorithm in this paper is effective by using the existing failure data provided by the laboratory of Guangdong Petrochemical Research Institute. Finally, some conclusions and application prospects were discussed.

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“…Some researchers used an optimization algorithm to optimize the parameter combination of the penalty factors and the number of decomposition in the VMD decomposition process. e genetic mutation particle swarm optimization (GMPSO) algorithm to optimize the VMD algorithm parameters is utilized by Ding [12,13]. Experimental results show that he GMPSO-VMD algorithm has a good decomposition effect on the gear fault signal.…”

Section: Introductionmentioning

confidence: 99%

Gear Fault Diagnosis Based on VMD Sample Entropy and Discrete Hopfield Neural Network

Ding

Xiao

Huang

et al. 2020

Mathematical Problems in Engineering

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The gear fault signal has some defects such as nonstationary nonlinearity. In order to increase the operating life of the gear, the gear operation is monitored. A gear fault diagnosis method based on variational mode decomposition (VMD) sample entropy and discrete Hopfield neural network (DHNN) is proposed. Firstly, the optimal VMD decomposition number is selected by the instantaneous frequency mean value. Then, the sample entropy value of each intrinsic mode function (IMF) is extracted to form the gear feature vectors. The gear feature vectors are coded and used as the memory prototype and memory starting point of DHNN, respectively. Finally, the coding vector is input into DHNN to realize fault pattern recognition. The newly defined coding rules have a significant impact on the accuracy of gear fault diagnosis. Driven by self-associative memory, the coding of gear fault is accurately classified by DHNN. The superiority of the VMD-DHNN method in gear fault diagnosis is verified by comparing with an advanced signal processing algorithm. The results show that the accuracy based on VMD sample entropy and DHNN is 91.67% of the gear fault diagnosis method. The experimental results show that the VMD method is better than the complete ensemble empirical mode decomposition with adaptive noise (CEEMDAN) and empirical mode decomposition (EMD), and the effect of it in the diagnosis of gear fault diagnosis is emphasized.

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GMPSO-VMD Algorithm and Its Application to Rolling Bearing Fault Feature Extraction

Cited by 50 publications

References 45 publications

Application of Parameter Optimized Variational Mode Decomposition Method in Fault Feature Extraction of Rolling Bearing

Application of Parameter Optimized Variational Mode Decomposition Method in Fault Feature Extraction of Rolling Bearing

A Classification Algorithm of Fault Modes-Integrated LSSVM and PSO with Parameters’ Optimization of VMD

Gear Fault Diagnosis Based on VMD Sample Entropy and Discrete Hopfield Neural Network

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