A sub-domain adaptive transfer learning base on residual network for bearing fault diagnosis

Wang, Chong-Yu; Zhu, Guangya; Liu, Tianyuan; Xie, Yunchuan; Zhang, Di

doi:10.1177/10775463211042976

Cited by 7 publications

(3 citation statements)

References 25 publications

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“…The cell state will determine the output of LSTM. Firstly, the output of the cell state is calculated using the formula shown in equation (5).…”

Section: Key Technologies Of Cag Frameworkmentioning

confidence: 99%

Automatic Code Generation for Android Applications Based on Improved Pix2code

Zou,

2024

JAIT

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With the expansion of the Internet market, the traditional software development method has been difficult to meet the market demand due to the problems of long development cycle, tedious work and difficult system maintenance. Therefore, to improve software development efficiency, this study uses residual networks and bidirectional long short-term memory networks to improve the Pix2code model. The experiment results show that after improving the visual module of the Pix2code model using residual networks, the accuracy of the training set improves from 0.92 to 0.96, and the convergence time is shortened from 3 hours to 2 hours. After using a bidirectional long short-term memory network to improve the language module and decoding layer, the accuracy and convergence speed of the model have also been improved. The accuracy of the training set grew from 0.88 to 0.92, and the convergence time was shortened by 0.5 hours. However, models improved by bidirectional long short-term memory networks might exhibit over-fitting, and thus this study uses Dropout and Xavier normal distribution to improve the memory network. The results validate that the training set accuracy of the optimized bidirectional long short-term memory network remains around 0.92, but the accuracy of the test set has improved to a maximum of 85%. Dropout and Xavier normal distributions can effectively improve the over-fitting problem of bidirectional long short-term memory networks. Although they can also decrease the model’s stability, their gain is higher. The training and testing accuracy of the Pix2code improved by residual network and bidirectional long short-term memory network are 0.95 and 0.82, respectively, while the code generation accuracy of the original Pix2code is only 0.77. The above data indicates that the improved Pix2code model has improved the accuracy and stability of code automatic generation.

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“…The cell state will determine the output of LSTM. Firstly, the output of the cell state is calculated using the formula shown in equation (5).…”

Section: Key Technologies Of Cag Frameworkmentioning

confidence: 99%

Automatic Code Generation for Android Applications Based on Improved Pix2code

Zou,

2024

JAIT

View full text Add to dashboard Cite

show abstract

“…Deep learning has attracted the attention of many scholars because of its efficient learning of complex structure and large sample and high-dimensional data (Jiang et al, 2021; Wang et al, 2021). In recent years, the convolutional neural network (CNN), generative adversarial network (GAN), recurrent neural network (RNN), and other deep neural networks have been widely used in natural image denoising (Vo et al, 2021), medical electrocardiogram (ECG) signal denoising (Chiang et al, 2019), and audio signal denoising (Doumanidis et al, 2021).…”

Section: Introductionmentioning

confidence: 99%

LN-MRSCAE: A novel deep learning based denoising method for mechanical vibration signals

Yang

Wang

et al. 2023

Journal of Vibration and Control

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Vibration signals are used to monitor the running state of mechanical equipment, but always suffer from a lot of noise in the acquisition process. In order to eliminate noise interference as much as possible, a multilevel residual convolution autoencoder network based noise learning method (LN-MRSCAE) is proposed in this paper. The LN-MRSCAE consists of a deep convolutional autoencoder network and multilevel residual, in which the learning noise module encodes and decodes the noise components, combining with a residual module to obtain the denoised signal. The multilevel residual structure is designed to address the gradient disappearance in network learning and improve the training speed. In specific, the noise learning replaces the traditional clean signal learning. Instead of learning the characteristics of the original clean signal, just learn the characteristics of the noise signal. The effectiveness of LN-MRSCAE network denoising is verified by classic analog signal and actual bearing vibration signals with different faults. The results show that the LN-MRSCAE model is superior to the latest models, and the noise component of the signal is obviously suppressed.

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“…Luo et al (2021) investigated a deep convolutional generative network for mechanical fault diagnosis. Wang et al (2021) presented a sub-domain adaption method based on deep transfer learning for bearing fault diagnosis. Xiong et al (2021) proposed a deep neural network using wavelet packet transform for machinery diagnostics.…”

Section: Introductionmentioning

confidence: 99%

A novel information fusion approach using weighted neural networks for intelligent multi-class diagnostics of rotating machinery with unseen working conditions

Zarchi

Shahgholi

2022

Journal of Vibration and Control

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Analytical diagnostics tools have been proved effective. However, these techniques are unable to be adaptively implemented for diverse machines with various defect modes. Big data-based diagnostics tools have been demonstrated efficient, but they exploit massive data with a high computational cost and time that is impractical for online condition monitoring. Furthermore, the health signals may be acquired under unseen working conditions in real machines. These are the major challenges for previous diagnostics methods and make them infeasible in real-world applications. So, a novel technique is proposed in this research to solve mentioned problems. The main novelty of this method is to fuse information of various processing functions for multi-sensor signals and to apply an efficient feature bank for performing an efficacious feature learning method. The other novelty of this research is to select transferable features for diagnostics of rotating machinery under unseen working conditions. Mean squared error function for the improvement of diagnostics accuracy and transferable feature space with semidefinite dimension for the improvement of diagnostics speed and better visualization for the source and target machines with different working conditions are minimized jointly via weighted neural networks. This approach is verified by two case studies of machinery vibration datasets for the robustness analysis of bearing multi-fault diagnosis problems under various operational conditions for the sensitivity analysis. Results indicate that the proposed algorithm achieves good performance in real rotating machinery diagnostics with unseen working conditions.

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A sub-domain adaptive transfer learning base on residual network for bearing fault diagnosis

Cited by 7 publications

References 25 publications

Automatic Code Generation for Android Applications Based on Improved Pix2code

Automatic Code Generation for Android Applications Based on Improved Pix2code

LN-MRSCAE: A novel deep learning based denoising method for mechanical vibration signals

A novel information fusion approach using weighted neural networks for intelligent multi-class diagnostics of rotating machinery with unseen working conditions

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