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Dg Softmax
DG-SoftmaxMany unsupervised domain adaptation models have been explored to tackle the fault transfer diagnosis issues. Nevertheless, their achievements completely rely on the availability of target domain samples during training. Unfortunately, th...…
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Maximum subspace transferability discriminant analysis
Maximum subspace transferability discriminant analysisIn the field of fault transfer diagnosis, many approaches only focus on the distribution alignment and knowledge transfer between the source domain and target domain. However, most of these app...…
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Heterogeneous federated domain generalization network
Heterogeneous federated domain generalization networkVarious federated transfer learning (FTL) methods have been proposed to address domain shift and safeguard data privacy in the field of fault diagnosis. However, the effectiveness of these metho...…
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Variance discrepancy representation
Variance discrepancy representationPlenty of maximum mean discrepancy (MMD)-based domain adaptation models have been applied to the fault transfer diagnosis. MMD uses the mean statistic in Hilbert space to measure the distribution discrepancy, whe...…
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Dense connection network with depthwise separable convolution(large model for rotating machine fault diagnosis)
Dense Connection Network With Depthwise Separable Convolution(Large model for rotating machine fault diagnosis)Most of the existing intelligent fault diagnosis models are suitable for only a type of rotating machine or equipment. To achieve the...…
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Gear life Cycle data download
We have performed gear fatigue test by the FZG gear contact fatigue test rig, and several Life-cycle vibration data sets have been acquired. The teeth numbers of gears 1, 2, 3, 4 are respectively 31, 25, 25 and 31. The download link is listed be...…
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Maximum mean square discrepancy
Maximum mean square discrepancyDiscrepancy representation metric completely determines the transfer diagnosis performance of deep domain adaptation methods. Maximum mean discrepancy (MMD) based on the mean statistic, as the commonly used metric, h...…
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Deep discriminative transfer learning network
Deep discriminative transfer learning networkMany domain adaptation methods have been presented to deal with the distribution alignment and knowledge transfer between the target domain and the source domain. However, most of them only pay attentio...…
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Multi Scale transfer voting mechanism
Multi-scale transfer voting mechanismDomain adaption models are widely applied to fault transfer diagnosis. However, the traditional domain adaption models can output only one high-dimension transfer feature (TF), thus it is difficult to capture d...…
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Gated dual attention unit(gdau)
Gated dual attention unit (GDAU)In the mechatronic system, rolling bearing is a frequently-used mechanical part, and its failure may result in serious accident and major economic loss. Therefore, the remaining useful life (RUL) prediction of rolli...…
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Reltanh dnn
ReLTanh DNNTanh is a sigmoidal activation function that suffers from vanishing gradient problem, so researchers have proposed some alternative functions including rectified linear unit (ReLU), however those vanishing-proof functions bring some oth...…
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Isigmoid dbn
ISigmoid DBNEfficient and accurate planetary gearbox fault diagnosis is the key to enhance the reliability and security of wind turbines. Therefore, an intelligent and integrated approach based on deep belief networks (DBNs), improved logistic Sig...…
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Dense framelet and its application into signal denoising
Dense framelet and its application into signal denoisingWavelet analysis has been widely applied to mechanical fault diagnosis. Aiming at the problems of current wavelet basis, such as low time-frequency sampling, asymmetry and poor shift-invarian...…
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Wavelet ridge signal decomposition
Wavelet ridge signal decompositionSignal decomposition is a widely-used approach for multicomponent signal processing. To improve the accuracy and anti-noise performance of multicomponent decomposition, this paper proposes a novel multicomponent s...…
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Transient feature extraction based on optimized morlet wavelet and kurtosis
Transient feature extraction based on optimized Morlet wavelet and kurtosisTo achieve the early fault diagnosis for rolling bearings, this paper proposes a new transient fault detection approach by the use of optimized Morlet wavelet transform, k...…
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Model Based impulsive wavelet and its sparse representation
Model-based impulsive wavelet and its sparse representationThe localized faults of rolling bearings can be diagnosed by the extraction of the impulsive feature. However, the approximately-periodic impulses may be submerged in strong interferences ...…
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Macro Micro attention Guided lstm for gear life prediction
Macro-Micro attention-guided LSTM for gear life predictionIn the mechanical transmission system, the gear is one of the most widely used transmission components. The fail-ure of the gear will cause serious accidents and huge economic loss. Therefo...…
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M Band flexible wavelet transform
M-band flexible wavelet transformThe fault diagnosis of planetary gear transmission systems is crucial for the safety of machineries and equipment. To identify the underlying fault features in measured signals, a novel M-band flexible wavelet tran...…
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Gear life Cycle data download
We have performed gear fatigue test by the FZG gear contact fatigue test rig, and several Life-cycle vibration data sets have been acquired. The teeth numbers of gears 1, 2, 3, 4 are respectively 31, 25, 25 and 31. The download link is listed be...…
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Fast 1d k Svd for transient feature extraction
Fast 1D K-SVD for transient feature extractionTo detect the incipient faults of rotating parts used in electromechanical systems widely, a novel transient feature extraction method based on the improved orthogonal matching pursuit (OMP) and one-di...…
