Research on fault diagnosis of time-domain vibration signal based on convolutional neural networks-Reference-Cited by-同舟云学术

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Research on fault diagnosis of time-domain vibration signal based on convolutional neural networks

Published:2019-09-05 Issue:3 Volume:7 Page:73-81
ISSN:2164-2583
Container-title:Systems Science & Control Engineering
language:en
Short-container-title:Systems Science & Control Engineering

Author:

Li Mingyong¹²,Wei Qingmin²,Wang Hongya¹,Zhang Xuekang³

Affiliation:

1. College of Computer Science and Technology, Donghua University, Shanghai, People’s Republic of China

2. College of Computer and Information Science, Chongqing Normal University, Chongqing, People’s Republic of China

3. College of Information Science and Technology, Donghua University, Shanghai, People’s Republic of China

Publisher

Informa UK Limited

Subject

Artificial Intelligence,Control and Optimization,Control and Systems Engineering

Link

https://www.tandfonline.com/doi/pdf/10.1080/21642583.2019.1661311

Reference16 articles.

1. Construction of hierarchical diagnosis network based on deep learning and its application in the fault pattern recognition of rolling element bearings

2. Improved Fault Diagnosis of Ball Bearings Based on the Global Spectrum of Vibration Signals

3. Convolutional Neural Network Based Fault Detection for Rotating Machinery

4. Deep neural networks: A promising tool for fault characteristic mining and intelligent diagnosis of rotating machinery with massive data

5. Bearing fault detection of induction motor using wavelet and Support Vector Machines (SVMs)

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1. Enhancing the accuracy of machinery fault diagnosis through fault source isolation of complex mixture of industrial sound signals;The International Journal of Advanced Manufacturing Technology;2024-07-05

2. New Correlation-function-based Method and Algorithm of Signal Processing for Fault Diagnosis of Rolling Bearings;2024 XXVII International Conference on Soft Computing and Measurements (SCM);2024-05-22

3. Enhancing the Accuracy of Machinery Fault Diagnosis through Fault Source Isolation of Complex Mixture of Industrial Sound Signals;2024-05-13

4. A priori information-guided generative adversarial network for data augmentation: application to pipeline fault diagnosis;Systems Science & Control Engineering;2024-04-30

5. Optimizing bearing health condition monitoring: exploring correlation feature selection algorithm;Engineering Research Express;2024-04-18

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