Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning

He, Deqiang; Liu, Chenyu; Jin, Zhenzhen; Ma, Rui; Chen, Yanjun; Shan, Sheng

doi:10.1016/j.energy.2021.122108

Cited by 116 publications

(46 citation statements)

References 36 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Extracting appropriate feature information is the key that determines the accuracy and reliability of fault diagnosis results. He et al [ 13 ] used an improved sparrow search algorithm to optimize the VMD parameters with dispersion entropy as the fitness value and used the optimized VMD algorithm to decompose the original signal into a series of mode components and calculate the energy entropy of each mode component to complete the flywheel bearing fault diagnosis. Xue et al [ 14 ] calculated the dispersion entropy of IMF components in different frequency bands and then used the joint approximate diagonalization of eigenmatrices (JADE) to extract fusion features and finally obtain the hierarchical discrete entropy (HDE) for bearing fault diagnosis.…”

Section: Introductionmentioning

confidence: 99%

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

Jin

Chen

Yang

2022

Entropy

View full text Add to dashboard Cite

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.

show abstract

Section: Introductionmentioning

confidence: 99%

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

Jin

Chen

Yang

2022

Entropy

View full text Add to dashboard Cite

show abstract

“…Although these two parameters can be directly pre-set by experience or experiments, the method has the drawback of blindness and is difficult to obtain excellent performance of VMD. Consequently, researchers usually utilize some intelligent optimization algorithms to determine the values of the two parameters, such as the genetic algorithm [ 24 , 25 ], particle swarm optimization [ 16 , 26 ], differential search algorithm [ 27 ], Archimedes optimization algorithm [ 28 ], grey wolf optimization [ 29 , 30 ], whale optimization algorithm [ 31 ], cuckoo search algorithm [ 32 ], sparrow search algorithm [ 33 ], and so on.…”

Section: Introductionmentioning

confidence: 99%

Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index

Guo

Yang

Jiang

et al. 2022

Sensors

View full text Add to dashboard Cite

Rolling bearing is an important part guaranteeing the normal operation of rotating machinery, which is also prone to various damages due to severe running conditions. However, it is usually difficult to extract the weak fault characteristic information from rolling bearing vibration signals and to realize a rolling bearing fault diagnosis. Hence, this paper offers a rolling bearing fault diagnosis method based on successive variational mode decomposition (SVMD) and the energy concentration and position accuracy (EP) index. Since SVMD decomposes a vibration signal of a rolling bearing into a number of modes, it is difficult to select the target mode with the ideal fault characteristic information. Comprehensively considering the energy concentration degree and frequency position accuracy of the fault characteristic component, the EP index is proposed to indicate the target mode. As the balancing parameter is crucial to the performance of SVMD and must be set properly, the line search method guided by the EP index is introduced to determine an optimal value for the balancing parameter of SVMD. The simulation and experiment results demonstrate that the proposed SVMD method is effective for rolling bearing fault diagnosis and superior to the variational mode decomposition (VMD) method.

show abstract

“…Rotating machines are the common mechanical component, which are widely used in subways, CNC machine tools, hydroelectric power plants, steam turbines and other devices [1][2][3]. Their health condition is associated with the equipment operates safely and stably.…”

Section: Introductionmentioning

confidence: 99%

Early intelligent fault diagnosis of rotating machinery based on IWOA-VMD and DMKELM

Jin

Lao

et al. 2022

Nonlinear Dyn

Self Cite

View full text Add to dashboard Cite

The effect of early fault vibration signals from rotating machinery is weak and easily drowned out by intensive noise. Thus, it is still a great challenge to perform early fault diagnosis. An intelligent early fault diagnosis method for rotating machines is proposed based on Variational Mode Decomposition (VMD) parameter optimization and Deep Multi-Kernel Extreme Learning Machine (DMKELM). First, the iterative chaotic mapping, nonlinear convergence factor and inertia weight are introduced into the whale optimization algorithm (WOA). Then, an improved WOA (IWOA) is presented and utilized to optimize VMD parameters. The optimized VMD (OVMD) combined with the sampling entropy is used to denoise and reconstruct the signals. Finally, the mish function is served as the activation function. Meanwhile, the radial basis kernel function (RBF) and polynomial kernel (PK) are introduced to construct the mixed kernel function. The mixed kernel assignment is applied to replace the random assignment, and the DMKELM is presented to enhance the classification performance and generalization capability of the model, and DMKELM is used for early intelligent fault diagnosis. Two experiments on bearings and gears demonstrated that the fault diagnosis accuracy is 99% and 98.5%, respectively, and the fault diagnosis accuracy is high. It shows that this method has great superiority in early fault diagnosis of rotating machinery.

show abstract

Fault diagnosis of flywheel bearing based on parameter optimization variational mode decomposition energy entropy and deep learning

Cited by 116 publications

References 36 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

Rolling Bearing Fault Diagnosis Based on Successive Variational Mode Decomposition and the EP Index

Early intelligent fault diagnosis of rotating machinery based on IWOA-VMD and DMKELM

Contact Info

Product

Resources

About