Feature Enhancement Method of Rolling Bearing Based on K-Adaptive VMD and RBF-Fuzzy Entropy

Jiao, Jing; Yue, Jianhai; Di, Pei

doi:10.3390/e24020197

Cited by 6 publications

(5 citation statements)

References 27 publications

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“…To verify the effectiveness of WOA in VMD parameter optimization, PSO-VMD and GA-VMD are used to compare and verify WOA-VMD, respectively. The initial parameters are as follows: the maximum iteration number is 40, the population size is 20, the average value of 20 tests is taken, the range of K is [2,10], and the range of α is [500, 6000]. The convergence comparison of the three optimization algorithms is shown in Figure 4.…”

Section: Methodsmentioning

confidence: 99%

“…Wang et al [ 9 ] used the Archimedes optimization algorithm (AOA) to optimize the mode number K and penalty factor α of the VMD algorithm by taking the minimum average value of all IMFs’ correlation waveform index (Cwi) as the objective function. Jiao et al [ 10 ] determined the mode number K required for VMD decomposition according to the method of abnormal decline of center frequency (ADCF). Duan et al [ 11 ] combined the improved VMD and sample entropy (SE) to determine the value of K by the maximum correntropy criterion (MCC), which effectively improved the statistical properties of highly nonlinear process errors.…”

Section: Introductionmentioning

confidence: 99%

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Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM

Jin

Chen

Yang

2022

Entropy

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In order to further improve the accuracy of fault identification of rolling bearings, a fault diagnosis method based on the modified particle swarm optimization (MPSO) algorithm optimized least square support vector machine (LSSVM), combining parameter optimization variational mode decomposition (VMD) and multi-scale permutation entropy (MPE), was proposed. Firstly, to solve the problem of insufficient decomposition and mode mixing caused by the improper selection of mode component K and penalty factor α in VMD algorithm, the whale optimization algorithm (WOA) was used to optimize the penalty factor and mode component number in the VMD algorithm, and the optimal parameter combination (K, α) was obtained. Secondly, the optimal parameter combination (K, α) was used for the VMD of the rolling bearing vibration signal to obtain several intrinsic mode functions (IMFs). According to the Pearson correlation coefficient (PCC) criterion, the optimal IMF component was selected, and its optimal multi-scale permutation entropy was calculated to form the feature set. Finally, K-fold cross-validation was used to train the MPSO-LSSVM model, and the test set was input into the trained model for identification. The experimental results show that compared with PSO-SVM, LSSVM, and PSO-LSSVM, the MPSO-LSSVM fault diagnosis model has higher recognition accuracy. At the same time, compared with VMD-SE, VMD-MPE, and PSO-VMD-MPE, WOA-VMD-MPE can extract more accurate features.

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Section: Methodsmentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM

Jin

Chen

Yang

2022

Entropy

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“…Entropy is a statistical measure, it can quantify the complexity and detect dynamic changes in time series and has been widely applied in bearing fault diagnosis [28,29]. Among various entropy methods, FuzzyEn is a popular entropy calculation method that was utilized in bearing fault detection and classification [30]. The specific calculation steps of FuzzyEn are listed as below [31].…”

Section: Fuzzy Entropymentioning

confidence: 99%

“…In Equation (30), Idc(•) represents the calculation of different indicators, namely Kurt(•), FuzzyEn(•), ApEn(•) and SampEn(•). The higher the change rate of a certain indicator, the stronger its sensitivity to noise, which means a better noise suppression effect when treating it as the OF in BD method.…”

Section: Proposal Of Feadmentioning

confidence: 99%

Fuzzy Entropy-Assisted Deconvolution Method and Its Application for Bearing Fault Diagnosis

Pei,

Yue,

Jiao

2024

Entropy

Self Cite

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Vibration signal analysis is an important means for bearing fault diagnosis. Affected by the vibration of other machine parts, external noise and the vibration transmission path, the impulses induced by a bearing defect in the measured vibrations are very weak. Blind deconvolution (BD) methods can counteract the effect of the transmission path and enhance the fault impulses. Most BD methods highlight fault features of the filtered signals by impulse-featured objective functions (OFs). However, residual noise in the filtered signals has not been well tackled. To overcome this problem, a fuzzy entropy-assisted deconvolution (FEAD) method is proposed. First, FEAD takes advantage of the high noise sensitivity of fuzzy entropy (FuzzyEn) and constructs a weighted FuzzyEn–kurtosis OF to enhance the fault impulses while suppressing noise interference. Then, the PSO algorithm is used to iteratively solve the optimal inverse deconvolution filter. Finally, envelope spectrum analysis is performed on the filtered signal to realize bearing fault diagnosis. The feasibility of FEAD was first verified by the bearing fault simulation signals at constant and variable speeds. The bearing test signals from Case Western Reserve University (CWRU), the railway wheelset and the test bench validated the good performance of FEAD in fault feature enhancement. A comparison with and quantitative results for the other state-of-the-art BD methods indicated the superiority of the proposed method.

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“…The value of the fuzzy entropy [31] is used to represent the signal complexity, and it uses the mean algorithm and the affiliation-function method to make the similarity measure between vectors fuzzy. The fuzzy entropy is similar to the theoretical properties of sample entropy and approximate entropy, and its fuzzy-entropy value is more stable than when the parameters are changed.…”

Section: Fuzzy Entropymentioning

confidence: 99%

Fault-Diagnosis Method for Rotating Machinery Based on SVMD Entropy and Machine Learning

Zhang

2023

Algorithms

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Rolling bearings and gears are important components of rotating machinery. Their operating condition affects the operation of the equipment. Fault in the accessory directly leads to equipment downtime or a series of adverse reactions in the system, which brings enormous pecuniary loss to the institution. Hence, it is of great significance to detect the operating status of rolling bearings and gears for fault diagnosis. At present, the vibration method is considered to be the most common method for fault diagnosis, a method that analyzes the equipment by collecting vibration signals. However, rotating-machinery fault diagnosis is challenging due to the need to select effective fault feature vectors, use appropriate machine-learning classification methods, and achieve accurate fault diagnosis. To solve this problem, this paper illustrates a new fault-diagnosis method combining successive variational-mode decomposition (SVMD) entropy values and machine learning. First, the simulation signal and the real fault signal are used to analyze and compare the variational-mode decomposition (VMD) and SVMD methods. The comparison results prove that SVMD can be a useful method for fault diagnosis. Then, these two methods are utilized to extract the energy entropy and fuzzy entropy of the gearbox dataset of Southeast University (SEU), respectively. The feature vector and multiple machine-learning classification models are constructed for failure-mode identification. The experimental-analysis results successfully verify the effectiveness of the combined SVMD entropy and machine-learning approach for rotating-machinery fault diagnosis.

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Feature Enhancement Method of Rolling Bearing Based on K-Adaptive VMD and RBF-Fuzzy Entropy

Cited by 6 publications

References 27 publications

Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM

Rolling Bearing Fault Diagnosis Based on WOA-VMD-MPE and MPSO-LSSVM

Fuzzy Entropy-Assisted Deconvolution Method and Its Application for Bearing Fault Diagnosis

Fault-Diagnosis Method for Rotating Machinery Based on SVMD Entropy and Machine Learning

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