Multichannel intelligent fault diagnosis of hoisting system using differential search algorithm‐variational mode decomposition and improved deep convolutional neural network

Li, Yang; Lee, Chi‐Guhn; Xu, Feiyun

doi:10.1002/stc.3023

Cited by 2 publications

(2 citation statements)

References 42 publications

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“…To verify the superior performance of the RWK index of the intrinsic modal component screening method proposed, RWK is compared with five new and effective methods, including CK [27], EK [28], MI [29], Min-EE [30] and Max-Ku [31].…”

Section: Bearing Fault Diagnosis Experiments and Results Analysismentioning

confidence: 99%

“…A series of modal components are obtained after VMD processing of bearing vibration signals. To screen out the intrinsic modal components containing abundant fault information, Li et al [27] adopted a correlation kurtosis (CK) index to process each modal component and reconstruct a new signal. Zhou et al [28] adopted ensemble kurtosis (EK) to select sensitive modal components for signal reconstruction.…”

Section: Introductionmentioning

confidence: 99%

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A new weak fault diagnosis approach for train bearings based on improved grey wolf optimizer and adaptive variational mode decomposition

Jin

et al. 2023

Meas. Sci. Technol.

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Accurately identifying the health status of train running gear bearings is crucial to en-sure the quality of operation. As the early fault information of bearings is weak and submerged in the complex noise environment, which is difficult to diagnose. Therefore, a new weak fault diagnosis approach for train running gear bearings based on variational mode decomposition (VMD) with improved performance and refined weighted kurtosis (RWK) index is proposed to solve this problem. First, an improved grey wolf optimizer (IGWO) based on a variety of strategies is proposed. Secondly, the VMD performance is improved using the IGWO algorithm, and the improved VMD is used to process the early weak signals of bearings. A new fault-sensitive index called the RWK is proposed to detect the mode with the most fault information. Finally, the envelope analysis of the characteristic signals is performed to achieve the early weak fault diagnosis of bearings. Compared with the other nine optimization algorithms, the IGWO algorithm has strong optimization ability, stable performance and a fast convergence speed. Four cases verify that the RWK index has the highest sensitivity to fault information and can more effec-tively filter out modal components containing rich fault information than the compari-son methods.

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Section: Bearing Fault Diagnosis Experiments and Results Analysismentioning

confidence: 99%

Section: Introductionmentioning

confidence: 99%

A new weak fault diagnosis approach for train bearings based on improved grey wolf optimizer and adaptive variational mode decomposition

Jin

et al. 2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

Intelligent fault diagnosis of storage stacking machinery under variable working conditions using attention-based adaptive multimodal feature fusion networks

Meng,

Li,

Xie

et al. 2024

Structural Health Monitoring

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Due to the harsh working environment of storage stacking machinery, the fault information of important components is significantly complex, which leads to the problem of low classification accuracy and high computational complexity of existing deep learning-based fault diagnosis methods. To alleviate the problem, this paper presents a novel architecture named attention-based adaptive multimodal feature fusion networks for intelligent fault diagnosis of storage stacking machinery, which is aimed at improving the diagnostic precision and robustness of feature fusion network and learning the broader feature representation. Firstly, the long short-term memory layer is introduced to extract the feature information of multiple time steps to improve the self-extraction ability of multi-temporal features. Then, the maximum temporal feature fusion module is utilized to highlight the recognizability of deep fusion features. Finally, a residual layer with spanning connections is added to increase the utilization and characterization capability of deep fusion features. Experimental results demonstrate the effectiveness and superiority of the proposed method in intelligent fault diagnosis of storage stacking machinery under variable working conditions compared with some state-of-the-art deep learning-based methodologies.

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Multichannel intelligent fault diagnosis of hoisting system using differential search algorithm‐variational mode decomposition and improved deep convolutional neural network

Cited by 2 publications

References 42 publications

A new weak fault diagnosis approach for train bearings based on improved grey wolf optimizer and adaptive variational mode decomposition

A new weak fault diagnosis approach for train bearings based on improved grey wolf optimizer and adaptive variational mode decomposition

Intelligent fault diagnosis of storage stacking machinery under variable working conditions using attention-based adaptive multimodal feature fusion networks

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