2022
DOI: 10.1002/stc.3023
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Multichannel intelligent fault diagnosis of hoisting system using differential search algorithm‐variational mode decomposition and improved deep convolutional neural network

Abstract: Nowadays, the feature extraction method of multichannel acoustic emission (AE) signal provides a solid research foundation for digital and intelligent fault diagnosis of the hoisting system. More specifically, AE signal collected from the hoisting system is generally characterized by nonlinear and non-stationary, thus making the traditional intelligent fault diagnosis methods cannot accurately extract the inherent fault features. To alleviate this problem and improve

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Cited by 2 publications
(2 citation statements)
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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%
See 1 more Smart Citation
“…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%