Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning

By Ansi Zhang, Shaobo Li, Yuxin Cui, Wanli Yang, Rongzhi Dong, Jianjun Hu. IEEE Access, 2019

349 citations recorded by the source corpus.

This paper focuses on bearing fault diagnosis with limited training data. A major challenge in fault diagnosis is the infeasibility of obtaining sufficient training samples for every fault type under all working conditions. Recently deep learning based fault diagnosis methods have achieved promising results. However, most of these methods require large amount of training data. In this study, we propose a deep neural network based few-shot learning approach for rolling bearing fault diagnosis with limited data. Our model is based on the siamese neural network, which learns by exploiting sample pairs of the same or different categories. Experimental results over the standard Case Western Reserve University (CWRU) bearing fault diagnosis benchmark dataset showed that our few-shot learning approach is more effective in fault diagnosis with limited data availability. When tested over different noise environments with minimal amount of training data, the performance of our few-shot learning model surpasses the one of the baseline with reasonable noise level. When evaluated over test sets with new fault types or new working conditions, few-shot models work better than the baseline trained with all fault types. All our models and datasets in this study are open sourced and can be downloaded from https://mekhub.cn/as/fault_diagnosis_with_few-shot_learning/.

Subfields: Control and Systems Engineering, Mechanical Engineering, Mechanics of Materials

View Paper: https://www.semanticscholar.org/paper/dee0aeab46e4f08b5fe33170a77c73fae6ca6bab

How researchers see this paper

The researchers whose trusted networks rank it highest, strongest advocate first. Every researcher gets a different order.

In Control and Systems Engineering

1. Dhiraj Neupane
2. Yihao Xue
3. Mengjie Huang
4. Jong-Hoon Seok
5. Rui Yang
6. Zidong Wang
7. Roberto Ferrero

In Mechanical Engineering

1. Dhiraj Neupane
2. Jong-Hoon Seok

In Mechanics of Materials

1. Dhiraj Neupane
2. Yihao Xue
3. Mengjie Huang
4. Jong-Hoon Seok
5. Rui Yang
6. Zidong Wang
7. Roberto Ferrero

Citations in the corpus

• Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, methodology): “[159] applied a Siamese neural network-based few-shot learning method to deal with cross-working condition fault diagnosis problems, and the performance of the proposed method was verified by the CWRU dataset in cross-working conditions.”
• Bearing Fault Detection and Diagnosis Using Case Western Reserve University Dataset With Deep Learning Approaches: A Review (2020, background): “In [142], the authors proposed a model for overcoming the problem of the requirement of a large amount of training data for bearing fault detection and created a model that works efficiently with limited training data.”