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A Fast bearing Fault diagnosis method based on lightweight Neural Network RepVGG

Published: 17 January 2023 Publication History

Abstract

In view of the shortcomings of existing deep learning methods in rolling bearing fault diagnosis, such as large number of training parameters and complex network, a fast rolling bearing fault diagnosis method based on lightweight neural network RepVGG was proposed. Firstly, the vibration signal is converted into three-channel time-frequency image by the combination of short-time Fourier transform (STFT) and pseudo-color processing technology, then the time-frequency image is inputted into the RepVGG network model for training. and the experiment is carried out on the case Western Reserve University (CWRU) data set. The accuracy is 99.62% and the training time is obviously lower than other popular fault diagnosis algorithm models based on deep learning. Finally, using the open source framework ncnn to deploy the RepVGG network model to the edge computing node Raspberry Pi, the average test accuracy is 95%, and the running efficiency is good.

References

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cover image ACM Other conferences
AISS '22: Proceedings of the 4th International Conference on Advanced Information Science and System
November 2022
396 pages
ISBN:9781450397933
DOI:10.1145/3573834
Permission to make digital or hard copies of all or part of this work for personal or classroom use is granted without fee provided that copies are not made or distributed for profit or commercial advantage and that copies bear this notice and the full citation on the first page. Copyrights for components of this work owned by others than ACM must be honored. Abstracting with credit is permitted. To copy otherwise, or republish, to post on servers or to redistribute to lists, requires prior specific permission and/or a fee. Request permissions from [email protected]

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Association for Computing Machinery

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Publication History

Published: 17 January 2023

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Author Tags

  1. bearing fault diagnosis
  2. deep learning
  3. ncnn
  4. re-parameterized VGG network (RepVGG)

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Overall Acceptance Rate 41 of 95 submissions, 43%

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Cited By

View all
  • (2024)YOLOv8-RCAA: A Lightweight and High-Performance Network for Tea Leaf Disease DetectionAgriculture10.3390/agriculture1408124014:8(1240)Online publication date: 27-Jul-2024
  • (2023)Embedded Yolo-Fastest V2-Based 3D Reconstruction and Size Prediction of Grain Silo-BagRemote Sensing10.3390/rs1519484615:19(4846)Online publication date: 7-Oct-2023
  • (2023)Generating Variable Explanations via Zero-Shot Prompt LearningProceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE56229.2023.00130(748-760)Online publication date: 11-Nov-2023
  • (2023)An Empirical Study of Parameter-Efficient Fine-Tuning Methods for Pre-Trained Code ModelsProceedings of the 38th IEEE/ACM International Conference on Automated Software Engineering10.1109/ASE56229.2023.00125(397-408)Online publication date: 11-Nov-2023

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