Fault Diagnosis of Rolling Bearing Based on GA-VMD and Improved WOA-LSSVM

Li, Junning; Chen, Wuge; Han, Ka; Wang, Qian

doi:10.1109/access.2020.3023306

Cited by 47 publications

(22 citation statements)

References 45 publications

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“…As shown in (3) METHOD PROPOSED IN PAPER [20] An overview of the rolling bearing failure mode recognition method based on VMD and energy entropy feature vector value of LSSVM is presented in this section [20].…”

Section: Methods Comparison (1) Different Fault Feature Vectorsmentioning

confidence: 99%

“…However, automatically selecting effective reconstruction order differs significantly in convergence accuracy of different vibration signals. In order to solve low convergence accuracy problem in parameter optimization of penalty factor α and decomposition level K, Li et al [20] proposed a combination of wavelet threshold de-noising, VMD genetic algorithm optimization, and whale optimization algorithm (WOA) of least squares support vector machine method (LSSVM). However, eigenvalue wavelet packet energy entropy lacked the ability to characterize bearing fault characteristics when exposed to various equipment conditions, with relatively weak corresponding generalization present.…”

Section: Introductionmentioning

confidence: 99%

See 1 more Smart Citation

A Method for Constructing Automatic Rolling Bearing Fault Identification Model Based on Refined Composite Multi-Scale Dispersion Entropy

et al. 2021

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In this paper, one of most widely utilized rolling bearings in rotating machinery is selected as the research object. Automatic rolling bearing fault identification model including support vector machine (SVM) training module, fault classification knowledge base module, and fault automatic identification module is proposed. A generalized method for automatic identification of rolling bearing faults based on refined composite multi-scale dispersion entropy (RCMDE) is developed. First, in order to solve the problem of setting the value range of the decomposition level K based on empirical knowledge for variational modal decomposition (VMD), a maximum kurtosis value method is proposed to determine the preset value range of K in whale optimization algorithm. Then, an improved VMD method is used to adaptively decompose the signal into a series of intrinsic mode function components. Next, the correlation coefficient method is employed to screen effective feature components of bearings in different health states for reconstruction. Through theoretical analysis, the calculated RCMDE value of reconstructed signal is screened and input as a feature value into the optimized SVM classifier for fault pattern recognition. The input of rolling bearing vibration data without preprocessing and the output of the fault identification which don't rely on empirical knowledge of external experts is realized. Experimental and engineering case data of rolling bearings under different equipment and operating environments are tested and validated. The results indicate that the model proposed in this paper shows good fault identification, demonstrates good generalization performance, and has beneficial industrial application prospect.

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Section: Methods Comparison (1) Different Fault Feature Vectorsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A Method for Constructing Automatic Rolling Bearing Fault Identification Model Based on Refined Composite Multi-Scale Dispersion Entropy

et al. 2021

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“…In 2019, a rolling bearing fault diagnosis method is proposed based on the fine-to-coarse multiscale permutation entropy (F2CMPE), laplacian score (LS), and SVM by Huo et al [56]. Next, a rolling bearing fault diagnosis method combining wavelet threshold de-noising, the genetic algorithm optimization variational mode decomposition (GA-VMD), and the von Neumann topology optimization least squares support vector machine (VNWOA-LSSVM) algorithm is proposed in 2020 [57]. In DL trends, a novel energy-fluctuated multiscale feature mining approach based on wavelet packet energy (WPE) image and deep convolutional network (ConvNet) for bearing fault diagnosis is proposed by X.…”

Section: ) Comparison Between E-bpso-svm With the Existing Modelsmentioning

confidence: 99%

An Enhanced Binary Particle Swarm Optimization for Optimal Feature Selection in Bearing Fault Diagnosis of Electrical Machines

Lee

2021

IEEE Access

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This study proposes an effective bearing fault diagnosis model based on an optimized approach for feature selection. The measured signal of the electric motor is processed by envelope analysis and Hilbert-Huang transform techniques to extract the potential features. An enhancement of the binary particle swarm optimization algorithm through population initialization strategy based on feature weights, new updating mechanism, and the screening and replacing process create a new and effective feature selection method that improves classification accuracy and reduces data size. The optimal feature subset is provided separately for artificial neural networks, and support vector machine classifier for the final recognition task. In multiple case studies, the proposed feature selection method is evaluated against the benchmark data sets and shows performance comparable to that of other peer competitors. The effectiveness of the proposed bearing fault diagnosis model is verified on different testbeds and achieves high accuracy and robustness under noise conditions. In addition, experimental results are compared with existing fault diagnostic models, showing the high possibility of the proposed bearing fault diagnosis model.

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“…The ranges of these two parameters were chosen from the experience accumulated in many previous papers. [34][35][36] The GA parameters used are as follows: the maximum number of iterations is 50, the population size is 10, the crossover probability is 0.8 and the mutation probability is 0.1. The parameter optimization flow chart is shown in Figure 1, and the optimization steps of K and α in the VMD algorithm are briefly described as follows:…”

Section: The Ga-vmd Algorithmmentioning

confidence: 99%

An optimized variational mode decomposition method and its application in vibration signal analysis of bearings

Peng

et al. 2021

Structural Health Monitoring

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The performance of the rolling bearing of a spindle device is directly related to the safety and reliability of the operation of a mine hoist. To extract bearing vibration signal features effectively for fault diagnosis, a feature extraction method based on the parameter optimization of a variational mode decomposition (VMD) method and permutation entropy (PE) is proposed. In addition, a support vector machine (SVM) classifier is used to identify bearing fault types. An analogue signal is used to test the effect of noise and sampling frequency on VMD performance. Focused on the problem of the VMD method needing to determine the number of mode components K and a penalty factor α during the signal decomposition process, a genetic algorithm is used to optimize the parameter combination [K,α] with the minimum sample entropy as the indicator. By using mean squared error (MSE) and correlation coefficient, an evaluation indicator is constructed to determine the decomposition effects of the optimized VMD, centre frequency, empirical mode decomposition (EMD) and ensemble EMD (EEMD) methods. The normalized PE of the five mode components is used as an eigenvalue, which is used as the input parameter of the SVM. Two different experimental datasets are used to verify the effectiveness of the proposed method. The results show that the proposed method has better diagnostic accuracy than EMD, EEMD and a BP neural network in the case of limited samples and unknown sample inputs. It can provide a good reference for the diagnosis of a rolling bearing and has practical application value.

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Fault Diagnosis of Rolling Bearing Based on GA-VMD and Improved WOA-LSSVM

Cited by 47 publications

References 45 publications

A Method for Constructing Automatic Rolling Bearing Fault Identification Model Based on Refined Composite Multi-Scale Dispersion Entropy

A Method for Constructing Automatic Rolling Bearing Fault Identification Model Based on Refined Composite Multi-Scale Dispersion Entropy

An Enhanced Binary Particle Swarm Optimization for Optimal Feature Selection in Bearing Fault Diagnosis of Electrical Machines

An optimized variational mode decomposition method and its application in vibration signal analysis of bearings

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