Intelligent Fault Diagnosis Method Based on Full 1-D Convolutional Generative Adversarial Network

Guo, Qingwen; Li, Yibin; Yan, Shuicheng; Wang, Daichao; Wu, Chen

doi:10.1109/tii.2019.2934901

Cited by 178 publications

(50 citation statements)

References 26 publications

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“…The advantage of DAE is that it can learn useful information from damaged data, and CAE learns more stable feature representations through penalty items. The latest AE model also combines with the GAN network to generate labeled samples [28][29][30] and also embeds the semisupervised learning method into the VAE model [31,32]. The Recurrent Neural Network has no advantage in classification, and it is more commonly used in mechanical life prediction.…”

Section: Introductionmentioning

confidence: 99%

Distilling the Knowledge of Multiscale Densely Connected Deep Networks in Mechanical Intelligent Diagnosis

Wang

Chen

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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At present, deep neural network (DNN) technology is often used in intelligent diagnosis research. However, the huge amount of calculation of DNN makes it difficult to apply in industrial practice. In this paper, an advanced multiscale dense connection deep network MSDC-NET is designed. A well-designed multiscale parallel branch module is used in the network. This module can greatly improve the acceptance domain of MSDC-NET, so as to learn useful information from input samples more effectively. Based on the inspiration of Densely Connected Convolutional Networks, MSDC-NET designed a similar dense connection technology, so that the model will not have the problem of gradient vanishing because of the deep network. The experimental data of MSDC-NET on MFPT, SEU, and Pu datasets show that our method has higher performance than other latest technologies. At the same time, we carried out knowledge distillation based on the high-precision classification level of MSDC-NET, which makes the diagnosis ability and robustness of the lightweight CNN model improve significantly.

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Section: Introductionmentioning

confidence: 99%

Distilling the Knowledge of Multiscale Densely Connected Deep Networks in Mechanical Intelligent Diagnosis

Wang

Chen

Zhang

et al. 2021

Wireless Communications and Mobile Computing

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“…In [ 30 ], Radford built the GAN layer structure by convolution and deconvolution to form the DCGAN algorithm, which greatly improves the performance of GAN. In [ 31 , 32 ], Guo and Gao both used 1DCNN to construct the layer structure of GAN and achieved better results in bearing fault diagnosis under the condition of an unbalanced dataset. Although DCGAN largely solves the problems of poor generation results and the long training time of GAN, the presence of large noise interference in the original signal and invalid feature information still leads to the limitations of DCGAN in dataset enhancement.…”

Section: Introductionmentioning

confidence: 99%

Fault Diagnosis Method for Rolling Mill Multi Row Bearings Based on AMVMD-MC1DCNN under Unbalanced Dataset

Zhao

Sun

Lin

et al. 2021

Sensors

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Rolling mill multi-row bearings are subjected to axial loads, which cause damage of rolling elements and cages, so the axial vibration signal contains rich fault character information. The vertical shock caused by the failure is weakened because multiple rows of bearings are subjected to radial forces together. Considering the special characters of rolling mill bearing vibration signals, a fault diagnosis method combining Adaptive Multivariate Variational Mode Decomposition (AMVMD) and Multi-channel One-dimensional Convolution Neural Network (MC1DCNN) is proposed to improve the diagnosis accuracy. Additionally, Deep Convolutional Generative Adversarial Network (DCGAN) is embedded in models to solve the problem of fault data scarcity. DCGAN is used to generate AMVMD reconstruction data to supplement the unbalanced dataset, and the MC1DCNN model is trained by the dataset to diagnose the real data. The proposed method is compared with a variety of diagnostic models, and the experimental results show that the method can effectively improve the diagnosis accuracy of rolling mill multi-row bearing under unbalanced dataset conditions. It is an important guide to the current problem of insufficient data and low diagnosis accuracy faced in the fault diagnosis of multi-row bearings such as rolling mills.

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“…Z.H.Chen [18] et al proposed Rolling Bearing Fault Diagnosis Using Time-Frequency Analysis and Deep Transfer Convolutional Neural Network. Q.W.Guo [19] et al proposed a multi-label one-dimensional generative confrontation network (ML1-D-GAN) model for fault diagnosis. P.F.Liang [20] et al proposed wavelet transform, generative adversarial network and convolutional neural network fault diagnosis models.…”

Section: Introductionmentioning

confidence: 99%

CvT Fault Diagnosis Method of Manifold Sensitive Modal Matrix Under Variable Speed

DeChen¹,

Li²,

Huang³

2021

Preprint

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Rotating machinery (RM) is one of the most common mechanical equipment in engineering applications and has a broad and vital role. Rotating machinery includes gearboxes, bearing motors, generators, etc. In industrial production, the important position of rotating machinery and its variable speed and complex working conditions lead to unstable vibration characteristics, which have become a research hotspot in mechanical fault diagnosis. Aiming at the multi-classification problem of rotating machinery with variable speed and complex working conditions, this paper proposes a fault diagnosis method based on the construction of improved sensitive mode matrix (ISMM), isometric mapping (ISOMAP) and Convolution-Vision Transformer network (CvT) structure. After overlapping and sampling the variable speed signals, a high-dimensional ISMM is constructed, and the ISMM is mapped into the manifold space through ISOMAP manifold learning. This method can extract the fault transient characteristics of the variable speed signal, and the experiment proves that it can solve the problem that the conventional method cannot effectively extract the characteristics of the variable speed data. CvT combines the advantages of self-attention mechanism and convolution in CNN, so the CvT network structure is used for feature extraction and fault recognition and classification. The CvT network structure takes into account both global feature extraction and local feature extraction, which greatly reduces the number of training iterations and the size of the network model. Two data sets (the HFXZ-I planetary gearbox variable speed data set in the laboratory and the bearing variable speed public data set of the University of Ottawa in Canada) are used to experimentally verify the proposed fault diagnosis model. Experimental results show that the proposed fault diagnosis model has good recognition accuracy and robustness.

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Intelligent Fault Diagnosis Method Based on Full 1-D Convolutional Generative Adversarial Network

Cited by 178 publications

References 26 publications

Distilling the Knowledge of Multiscale Densely Connected Deep Networks in Mechanical Intelligent Diagnosis

Distilling the Knowledge of Multiscale Densely Connected Deep Networks in Mechanical Intelligent Diagnosis

Fault Diagnosis Method for Rolling Mill Multi Row Bearings Based on AMVMD-MC1DCNN under Unbalanced Dataset

CvT Fault Diagnosis Method of Manifold Sensitive Modal Matrix Under Variable Speed

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