Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis

Zhao, Ke; Jiang, Hongkai; Wang, Kaibo; Pei, Zeyu

doi:10.1016/j.knosys.2021.106974

Cited by 149 publications

(50 citation statements)

References 35 publications

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“…rough continuous training and learning of sample data, a complete and reliable equipment state database is established, and finally, effective distribution terminal operation state identification is realized [20][21][22]. However, it should be noted that there is less research on realizing the state perception of distribution terminal based on deep learning, but many scholars have effectively perceived the operation state of power equipment or mechanical equipment with the help of the powerful learning ability of multilayer network.…”

Section: Related Researchmentioning

confidence: 99%

State Evaluation Method of Distribution Terminal Based on Deep Reinforcement Learning

Xue¹,

Li²,

Wang³

et al. 2022

Mathematical Problems in Engineering

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The reliable operation of the distribution terminal is a key link to realize distribution automation. It is particularly important to efficiently and accurately evaluate the operation state of the distribution terminal. In order to realize accurate state perception of distribution terminals, a state evaluation method based on deep reinforcement learning is proposed to support the reliable operation of the distribution network. First, the fault causes of terminal equipment and the collected datasets are introduced. On this basis, the multilayer network structure is used to analyze the terminal state. Q-reinforcement learning network is used to optimize the convolution neural network, solve the overfitting problem of the deep network model, and continuously extract the data features. At the same time, in order to increase the objectivity and reliability of the evaluation method, the membership function optimization is also introduced into the model to further ensure the accuracy of the state analysis method. Simulation results show that the recognition accuracy of the proposed method is 94.23%, which shows excellent evaluation performance.

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Section: Related Researchmentioning

confidence: 99%

State Evaluation Method of Distribution Terminal Based on Deep Reinforcement Learning

Xue¹,

Li²,

Wang³

et al. 2022

Mathematical Problems in Engineering

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“…erefore, an e ective rotating machinery condition monitoring and fault identi cation system are established to ensure the safe operation of equipment and personnel safety. As the most basic component, bearings frequently work in harsh environments and complex working conditions, and its health status a ects seriously the working e ciency [7][8][9][10]. e health statuses of rolling bearing can not only reduce equipment maintenance costs but also contribute to reducing major accidents [11,12].…”

Section: Introductionmentioning

confidence: 99%

An Enhanced Gated Recurrent Unit-Based Adaptive Fault Diagnosis of Rotating Machinery

Riaz

Muhammad

et al. 2022

Shock and Vibration

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As the most basic component of rotating machinery, rolling bearing frequently works in harsh environments and complex working conditions, and its health status affects seriously the working efficiency. The health statuses of rolling bearing can not only reduce equipment maintenance costs but also contribute to reducing major accidents. Based on this, an adaptive diagnosis method that combines deep gated recurrent unit (DGRU) with wavelet packet decomposition (WPD) and extreme learning machine (ELM) is proposed for rolling bearing. Firstly, WPD is utilized to eliminate the noise of data. Secondly, DGRU is designed to extract the representative features of denoised data. Finally, ELM is utilized to output the diagnosis results. Massive results prove that the superiority and robustness of our approach outperform existing popular methods. Additionally, the proposed method can also achieve powerful antinoise ability.

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“…Recently, transfer learning (TL) has greatly improved the performance of many real-world applications in computer imaging and natural language processing [14][15][16]. The needs of TL occur when there was a limited labelled target domain dataset; while the availability of a related source domain dataset is sufficient to establish a learning model.…”

Section: Introductionmentioning

confidence: 99%

Transferring Near Infrared Spectroscopic Calibration Model Across Different Harvested Seasons Using Joint Distribution Adaptation

Suarin¹,

Chia²

2022

Lecture Notes in Electrical Engineering

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Joint distribution adaptation network with adversarial learning for rolling bearing fault diagnosis

Cited by 149 publications

References 35 publications

State Evaluation Method of Distribution Terminal Based on Deep Reinforcement Learning

State Evaluation Method of Distribution Terminal Based on Deep Reinforcement Learning

An Enhanced Gated Recurrent Unit-Based Adaptive Fault Diagnosis of Rotating Machinery

Transferring Near Infrared Spectroscopic Calibration Model Across Different Harvested Seasons Using Joint Distribution Adaptation

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