Time-Frequency Sparse Reconstruction of Non-Uniform Sampling for Non-Stationary Signal

Dong, Jiannan; Li, Hongkun; Fan, Zhenfang; Zhao, Xinwei

doi:10.1109/tvt.2021.3111213

Cited by 18 publications

(6 citation statements)

References 37 publications

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“…Figure 8 displays the time signal and time-frequency spectrum of the 0.007-inch-fault bearing signal with five different amounts of additional noise, ranging from −6 dB to 6 dB, in order to better highlight the attributes of noisy signals in various domains. The spectrum is produced using short-time Fourier transform (STFT) over time windows [ 38 ], where the time outputs correspond to time window centres. As shown in Figure 8 , the spectrogram is clearly capable of revealing certain fault features, which are difficult to differentiate from jumbled time signals, and collecting vital visual information regarding bearing fault.…”

Section: Experimental Validationmentioning

confidence: 99%

Intelligent Bearing Fault Diagnosis Based on Feature Fusion of One-Dimensional Dilated CNN and Multi-Domain Signal Processing

Dong

Lotfipoor

2023

Sensors

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Finding relevant features that can represent different types of faults under a noisy environment is the key to practical applications of intelligent fault diagnosis. However, high classification accuracy cannot be achieved with only a few simple empirical features, and advanced feature engineering and modelling necessitate extensive specialised knowledge, resulting in restricted widespread use. This paper has proposed a novel and efficient fusion method, named MD-1d-DCNN, that combines statistical features from multiple domains and adaptive features retrieved using a one-dimensional dilated convolutional neural network. Moreover, signal processing techniques are utilised to uncover statistical features and realise the general fault information. To offset the negative influence of noise in signals and achieve high accuracy of fault diagnosis in noisy settings, 1d-DCNN is adopted to extract more dispersed and intrinsic fault-associated features, while also preventing the model from overfitting. In the end, fault classification based on fusion features is accomplished by the usage of fully connected layers. Two bearing datasets containing varying amounts of noise are used to verify the effectiveness and robustness of the suggested approach. The experimental results demonstrate MD-1d-DCNN’s superior anti-noise capability. When compared to other benchmark models, the proposed method performs better at all noise levels.

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Section: Experimental Validationmentioning

confidence: 99%

Intelligent Bearing Fault Diagnosis Based on Feature Fusion of One-Dimensional Dilated CNN and Multi-Domain Signal Processing

Dong

Lotfipoor

2023

Sensors

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“…In addition, after going through the fully connected layer and Softmax classifier, we obtain the predicted category information, combined with the label information, the cross-entropy loss can be calculated, denoted as 𝐿 𝐶 , as shown in Eq. (6).…”

Section: The Mk-mmd-based Transfer Diagnosis Modelmentioning

confidence: 99%

A wavelet packet transform-based deep feature transfer learning method for bearing fault diagnosis under different working conditions

Liang

Wang

et al. 2022

Measurement

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“…The traditional vibration signal analysis methods, such as short time Fourier transform (STFT) [8], wavelet packet transform (WPT) [9], empirical mode decomposition (EMD) [10] and singular value transform (SVD) [11], can process and analyze non-stationary and nonlinear vibration signals, but they cannot fully extract the rich information of fault features contained in the signal, causing the traditional machine learning diagnosis methods unable to meet the requirements of accurate bearing fault classification [12][13][14][15].…”

Section: Introductionmentioning

confidence: 99%

A Width Multi-Scale Adversarial Domain Adaptation Residual Network with AConvolutional Block Attention Module

Yin

Sun

et al. 2023

Preprint

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Although the fault diagnosis methods based on deep learning have attracted widespread attention in the academic field in recent years, such methods still face many challenges, including complex and variable working conditions, insufficient ability to extract key features, and large differences in sample data. To address these problems, a width multi-scale adversarial domain adaptation residual network with a convolutional block attention module (WMSRCIDANN) is proposed in this paper, which consists of a feature extraction network, a domain discriminant network, and a label classification network. In the feature extraction network, an improved width multi-scale residual network combined with a convolutional block attention module (WMSRC) is used as the feature extractor to achieve a weighted fusion of multi-depth features.In the domain discriminative network, the fully-connected network is replaced by a four-layer convolutional structure, which can further reduce the difference in feature distribution and improve the cross-domain invariance of deep features. In the label classification network, the classifier uses the extracted domain-invariant features to perform cross-domain fault identification. The experimental results show that WMSRCIDANN is effective in cross-domain bearing fault diagnosis.

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Time-Frequency Sparse Reconstruction of Non-Uniform Sampling for Non-Stationary Signal

Cited by 18 publications

References 37 publications

Intelligent Bearing Fault Diagnosis Based on Feature Fusion of One-Dimensional Dilated CNN and Multi-Domain Signal Processing

Intelligent Bearing Fault Diagnosis Based on Feature Fusion of One-Dimensional Dilated CNN and Multi-Domain Signal Processing

A wavelet packet transform-based deep feature transfer learning method for bearing fault diagnosis under different working conditions

A Width Multi-Scale Adversarial Domain Adaptation Residual Network with AConvolutional Block Attention Module

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