Research on fault diagnosis method of rolling bearing based on 2DCNN

Peng, Xingjie; Zhang, Beike; Gao, Dong

doi:10.1109/ccdc49329.2020.9164026

Cited by 11 publications

(8 citation statements)

References 8 publications

Supporting

Mentioning

Contrasting

Order By: Relevance

“…Yang et al [38] used the pre-trained ResNet18 for feature extraction and calculated the multiple kernel-MMD distances to match the difference in edge distribution, and then input the features extracted from different residual blocks into the classifier to output pseudo labels, thus the difference in conditional distribution was reduced. Peng et al [39] believed that when the number of samples was too large, the cost of using MMD to calculate distribution differences was huge. So, they used Wasserstein distance to measure domain differences to reduce the calculation cost.…”

Section: Domain Adaptation In the Fault Diagnosis Fieldmentioning

confidence: 99%

Mechanical fault diagnosis based on deep transfer learning: a review

Yang

Zhang

Yong-zheng

2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

Mechanical fault diagnosis is an important method to accurately identify the health condition of mechanical equipment and ensure its safe operation. With the advent of the era of "big data", it is an inevitable trend to choose deep learning for mechanical fault diagnosis. At the same time, to improve the generalization ability of deep learning applications in different scenarios of fault diagnosis, mechanical diagnosis based on transfer learning has also been proposed and become an important branch in the field of mechanical fault diagnosis. This paper introduces the principle of transfer learning, summarizes the research and application of transfer learning in the field of fault diagnosis, discusses the shortcomings of transfer learning in the field of fault diagnosis, and discusses the future research direction of transfer learning in the field of fault diagnosis.

show abstract

Section: Domain Adaptation In the Fault Diagnosis Fieldmentioning

confidence: 99%

Mechanical fault diagnosis based on deep transfer learning: a review

Yang

Zhang

Yong-zheng

2023

Meas. Sci. Technol.

View full text Add to dashboard Cite

show abstract

“…The first convolutional layer extracts low-level features, such as edges, lines, and corners. Higherlevel convolutional layers extract higher-level features [34]. As shown in Figure 4, the convolutional kernel slides on the input image sequentially, and the sliding direction is from left to right and from top to bottom.…”

Section: D-cnn Algorithmmentioning

confidence: 99%

Inversion Method for Multiple Nuclide Source Terms in Nuclear Accidents Based on Deep Learning Fusion Model

et al. 2023

View full text Add to dashboard Cite

During severe nuclear accidents, radioactive materials are expected to be released into the atmosphere. Estimating the source term plays a significant role in assessing the consequences of an accident to assist in actioning a proper emergency response. However, it is difficult to obtain information on the source term directly through the instruments in the reactor because of the unpredictable conditions induced by the accident. In this study, a deep learning-based method to estimate the source term with field environmental monitoring data, which utilizes the bagging method to fuse models based on the temporal convolutional network (TCN) and two-dimensional convolutional neural network (2D-CNN), was developed. To reduce the complexity of the model, the particle swarm optimization algorithm was used to optimize the parameters in the fusion model. Seven typical radionuclides (Kr-88, I-131, Te-132, Xe-133, Cs-137, Ba-140, and Ce-144) were set as mixed source terms, and the International Radiological Assessment System was used to generate model training data. The results indicated that the average prediction error of the fusion model for the seven nuclides in the test set was less than 10%, which significantly improved the estimation accuracy compared with the results obtained by TCN or 2D-CNN. Noise analysis revealed the fusion model to be robust, having potential applicability toward more complex nuclear accident scenarios.

show abstract

“…Under the condition of ensuring accuracy, it reduces the calculation time and improves the global diagnosis efficiency, but there is a great requirement for the data sample capacity. In terms of reducing the data sample capacity, Du [11] proposed an improved diagnosis technology of the bearing of the multi-scale high-dimensional convolutional neural network of the S transform, which can improve the signal timefrequency characteristics and provide excellent training samples but can't optimize the neural network model.…”

Section: Introductionmentioning

confidence: 99%

Fault diagnosis method of hot die forging press based on short-time Fourier transform and optimized convolutional neural network

Yuan

Ling

et al. 2023

J. Phys.: Conf. Ser.

View full text Add to dashboard Cite

Aiming at the vibration signals of rolling bearings of a hot die forging press, which are nonlinear, unstable, and easily disturbed by strong background noise, a fault diagnosis method based on a short-time Fourier transform and optimized convolutional neural network was proposed. First, the Fourier transform was applied to the vibration signals of rolling bearings of a hot die forging press to obtain complete time-frequency samples. After spectrum compression, it is input into the convolutional neural network model. By optimizing activation function and adjusting network parameters, the purpose of efficient and fast detection in the case of small samples can be achieved. Through the simulation test, the feasibility and effectiveness are verified. The results show that the proposed method has high recognition accuracy for fault diagnosis of rolling bearings of hot die forging press.

show abstract

Research on fault diagnosis method of rolling bearing based on 2DCNN

Cited by 11 publications

References 8 publications

Mechanical fault diagnosis based on deep transfer learning: a review

Mechanical fault diagnosis based on deep transfer learning: a review

Inversion Method for Multiple Nuclide Source Terms in Nuclear Accidents Based on Deep Learning Fusion Model

Fault diagnosis method of hot die forging press based on short-time Fourier transform and optimized convolutional neural network

Contact Info

Product

Resources

About