Fault Diagnosis of Intershaft Bearing Using Variational Mode Decomposition with TAGA Optimization

Jun, Tian; Wang, Shuguang; Zhou, Jie; Ai, Yanting; Zhang, Yuwei; Fei, Chengwei

doi:10.1155/2021/8828317

Cited by 8 publications

(6 citation statements)

References 38 publications

(42 reference statements)

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“…Due to the characteristics of deep neural networks, the selection of hyperparameters in the model is still very important [23][24][25]. To this end, genetic algorithm [26], cuckoo algorithm [27], gray wolf algorithm [28] and other methods have been used for hyperparameter optimization to improve the performance of neural networks.…”

Section: Introductionmentioning

confidence: 99%

A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis

Zeng

et al. 2022

Applied Sciences

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Although some traditional autoencoders and their extensions have been widely used in the research of intelligent fault diagnosis of rotating parts, their feature extraction capabilities are limited without label information. In response to this problem, this research proposes a hierarchical sparse discriminant autoencoder (HSDAE) method for fault diagnosis of rotating components, which is a new semi-supervised autoencoder structure. By considering the sparsity of autoencoders, a hierarchical sparsity strategy was proposed to improve the stacked sparsity autoencoders, and the particle swarm optimization algorithm was used to obtain the optimal sparsity parameters to improve network performance. In order to enhance the classification of the autoencoder, a class aggregation and class separability strategy was used, which is an additional discriminative distance that was added as a penalty term in the loss function to enhance the feature extraction ability of the network. Finally, the reliability of the proposed method was verified on the bearing data set of Case Western Reserve University and the bearing data set of the laboratory test platform. The results of comparison with other methods show that the HSDAE method can enhance the feature extraction ability of the network and has reliability and stability for different data sets.

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Section: Introductionmentioning

confidence: 99%

A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis

Zeng

et al. 2022

Applied Sciences

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“…Thus, the stochastic configuration network (SCN) with a new way of assigning the random parameters by inequality constraints and adaptively selecting the scope of the random parameters is proposed in References 31,32. SCN is established by gradually obtaining its weight and deviation according to input data under a rigorous supervision mechanism 33,34 . This constructive approach avoids the disadvantages caused by fixed parameters or deficient supervisory mechanisms in other networks.…”

Section: Introductionmentioning

confidence: 99%

“…SCN is established by gradually obtaining its weight and deviation according to input data under a rigorous supervision mechanism. 33,34 This constructive approach avoids the disadvantages caused by fixed parameters or deficient supervisory mechanisms in other networks. Hence, the SCN has higher approximation precision and better generalization ability for characteristic recognition of incipient faults.…”

Section: Introductionmentioning

confidence: 99%

A novel collaborative diagnosis approach of incipient faults based on VMD and SCN for rolling bearing

Huang

Guan

et al. 2021

Optim Control Appl Methods

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In order to overcome the shortcomings of concealed early fault features of rolling bearings which can not be better recognized and the accuracy of early fault diagnosis is not high enough, a novel collaborative diagnosis method is presented combing with variational modal decomposition (VMD) and stochastic configuration network (SCN) for incipient faults of rolling bearing. First, decomposing the original signal by VMD, and then extracting peak-to-peak of intrinsic mode function component from each fault, and calculating sample entropy of peak-to-peak to construct characteristic sample of fault. Second, based on VMD composition, proposing an incipient fault diagnosis method which is named SCN. Finally, compared with other classification methods, the results show that the proposed collaborative method is effective and advantageous.

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“…By effectively enhancing the fault characteristics of these two kinds of faults, the ARPD calculated from vibration signals is used to complete the hypothesis testing. To extract the weak signal fault characteristics of aeroengine intermediate shaft bearing effectively, Jing et al [12] introduced a tolerance idea into the traditional adaptive genetic algorithm and proposed a variational mode decomposition (VMD) method based on TAGA-VMD. Machine learning and neural network methods have also become a research hotspot recently [13][14][15][16][17][18].…”

Section: Introductionmentioning

confidence: 99%

Fault Prediction of Centrifugal Pump Based on Improved KNN

Chen

Yuan

Luo

et al. 2021

Shock and Vibration

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To effectively predict the faults of centrifugal pumps, the idea of machine learning k-nearest neighbor algorithm (KNN) was introduced into the traditional Mahalanobis distance fault discrimination, and an improved centrifugal pump fault prediction model of KNN based on the Mahalanobis distance is proposed. In this method, the Mahalanobis distance is used to replace the distance function in the conventional KNN algorithm. Grid search and cross-validation are used to determine the optimal K value of the prediction model. A centrifugal pump test rig was established to solve three common faults of centrifugal pumps: cavitation, impeller damage, and machine seal damage, and the method was verified. The results show that this method can effectively distinguish the specific fault types of centrifugal pumps based on vibration signals, and the fault prediction accuracy of the off-balance condition is up to 82%. This study provides a novel idea and method for centrifugal pump fault prediction and diagnosis and avoids the interaction between parameters when monitoring multiple parameters.

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Fault Diagnosis of Intershaft Bearing Using Variational Mode Decomposition with TAGA Optimization

Cited by 8 publications

References 38 publications

A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis

A Hierarchical Sparse Discriminant Autoencoder for Bearing Fault Diagnosis

A novel collaborative diagnosis approach of incipient faults based on VMD and SCN for rolling bearing

Fault Prediction of Centrifugal Pump Based on Improved KNN

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