skip to main content
10.1145/3641343.3641407acmotherconferencesArticle/Chapter ViewAbstractPublication PagesiceitsaConference Proceedingsconference-collections
research-article

Fault Diagnosis of Bearing with Small Sample based on Siamese Networks and Metric Learning

Published: 29 April 2024 Publication History

Abstract

Bearing faults, characterized by their low probability occurrence and limited sample availability, present challenges in accurate diagnosis. Traditional data-driven diagnostic methods exhibit reduced accuracy and generalization in small datasets. This paper presents a Siamese networks model based on metric learning, which is used for bearing faults classification. Vibration signals are preprocessed using continuous wavelet transform (CWT), and features are extracted through the MobileNetV3 backbone network. The model employs Euclidean distance measurement for training and classification, demonstrating high accuracy under limited sample data.

References

[1]
EREN L, INCE T, KIRANYAZ S, 2019. A generic intelligent bearing fault diagnosis system using compact adaptive 1d CNN classifier. Journal of Signal Processing Systems, 91:179-189.
[2]
LEI Y G, YANG B, JIANG X W, Nandi. 2020. Applications of machine learning to machine fault diagnosis:a review and roadmap. Mechanical Systems and Signal Processing,138.
[3]
Liu R, Yang B, Zio E, 2018. Artificial intelligence for fault diagnosis of rotating machinery: A review. Mechanical Systems and Signal Processing (S1096-1216), 108: 33-47.
[4]
VOULODIMOS A, DOULAMIS N, DOULAMIS A, 2018. Deep learning for computer vision: A brief review. Computational Intelligence and Neuroscience, 2018: 7068349
[5]
TIAN C W, FEI L K, ZHENG W X, 2020. Deep learning on image denoising: an overview. Neural Networks, 131: 251-275.
[6]
YOUNG T, HAZARIKA D, PORIA S, 2018. Recent trends in deep learning based natural language processing. IEEE Computational Intelligence Magazine, 13(3): 55-75.
[7]
WEN L, LI X Y, GAO L, 2018. A new convolutional neural network-based data-driven fault diagnosis method. IEEE Transactions on Industrial Electronics, 65(7):5990-5998.
[8]
LI Y B, SI S B, LIU Z L, 2019. Review of local mean decomposition and its application in fault diagnosis of rotating machinery . Journal of Systems Engineering and Electronics, 30(4): 799-814.
[9]
HU T, TANG T, LIN R, 2020. A simple data augmentation algorithm and a self-adaptive convolutional architecture for few-shot fault diagnosis under different working conditions, Measurement, 156:107539.
[10]
CHICCO D. 2021. Siamese neural networks:an overview, Artificial Neural Networks, 2190:73-94
[11]
KOCH G, ZEMEL R, SALAKHUTDINOV R. 2015. Siamese neural networks for one-shot image recognition. Proceedings of the ICML Deep Learning Workshop.
[12]
ZHANG Y, PARDO B, DUAN Z. 2018. Siamese style convolutional neural networks for sound search by vocal imitation. IEEE/ACM Transactions on Audio, Speech, and Language Processing, 27(2):429-441
[13]
AHRABIAN K, BABAALI B. 2019. Usage of auto encoders and Siamese networks for online handwritten signature verification. Neural Computing and Applications, 31(12):9321-9334.
[14]
Howard AG, Zhu ML, Chen B, 2017. MobileNet-Efficient convolutional neural networks for mobile vision applications. arXiv:1704.04861
[15]
Sandler M, Howard A, Zhu ML, 2018. MobileNetV2: Inverted residuals and linear bottlenecks. Proceedings of 2018 IEEE/CVF Conference on Computer Vision and Pattern Recognition. Salt Lake City: IEEE, 4510–4520
[16]
Howard A, Sandler M, Chen B, 2019. Searching for MobileNetV3. Proceedings of the 2019 IEEE/CVF International Conference on Computer Vision (ICCV). Seoul: IEEE,1314–1324.

Index Terms

  1. Fault Diagnosis of Bearing with Small Sample based on Siamese Networks and Metric Learning
          Index terms have been assigned to the content through auto-classification.

          Recommendations

          Comments

          Information & Contributors

          Information

          Published In

          cover image ACM Other conferences
          ICEITSA '23: Proceedings of the 3rd International Conference on Electronic Information Technology and Smart Agriculture
          December 2023
          541 pages
          ISBN:9798400716775
          DOI:10.1145/3641343
          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 the author(s) 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].

          Publisher

          Association for Computing Machinery

          New York, NY, United States

          Publication History

          Published: 29 April 2024

          Permissions

          Request permissions for this article.

          Check for updates

          Qualifiers

          • Research-article
          • Research
          • Refereed limited

          Funding Sources

          • scientific research project of Key Laboratory of Intelligent Control Technology for Wuling Mountain Ecological Agriculture in Hunan Province
          • Scientific research project of the Education Department of Hunan Province

          Conference

          ICEITSA 2023

          Contributors

          Other Metrics

          Bibliometrics & Citations

          Bibliometrics

          Article Metrics

          • 0
            Total Citations
          • 8
            Total Downloads
          • Downloads (Last 12 months)8
          • Downloads (Last 6 weeks)2
          Reflects downloads up to 18 Feb 2025

          Other Metrics

          Citations

          View Options

          Login options

          View options

          PDF

          View or Download as a PDF file.

          PDF

          eReader

          View online with eReader.

          eReader

          HTML Format

          View this article in HTML Format.

          HTML Format

          Figures

          Tables

          Media

          Share

          Share

          Share this Publication link

          Share on social media