An intelligent fault diagnosis approach based on transfer learning from laboratory bearings to locomotive bearings

By Bin Yang, Yaguo Lei, Feng Jia, Saibo Xing. Mechanical systems and signal processing, 2019

758 citations recorded by the source corpus.

Abstract Intelligent fault diagnosis of rolling element bearings has made some achievements based on the availability of massive labeled data. However, the available data from bearings used in real-case machines (BRMs) are insufficient to train a reliable intelligent diagnosis model. Fortunately, we can easily simulate various faults of bearings in a laboratory, and the data from bearings used in laboratory machines (BLMs) contain diagnosis knowledge related to the data from BRMs. Therefore, inspired by the idea of transfer learning, we propose a feature-based transfer neural network (FTNN) to identify the health states of BRMs with the help of the diagnosis knowledge from BLMs. In the proposed method, a convolutional neural network (CNN) is employed to extract transferable features of raw vibration data from BLMs and BRMs. Then, the regularization terms of multi-layer domain adaptation and pseudo label learning are developed to impose constraints on the parameters of CNN so as to reduce the distribution discrepancy and the among-class distance of the learned transferable features. The proposed method is verified by two fault diagnosis cases of bearings, in which the health states of locomotive bearings in real cases are identified by using the data respectively collected from motor bearings and gearbox bearings in laboratories. The results show that the proposed method is able to effectively learn transferable features to bridge the discrepancy between the data from BLMs and BRMs. Consequently, it presents higher diagnosis accuracy for BRMs than existing methods.

Subfields: Control and Systems Engineering, Mechanical Engineering, Radiological and Ultrasound Technology

View Paper: https://www.semanticscholar.org/paper/8fb8ca7c80914a5477a10b2614223045deb622d9

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. Yihao Xue
2. Ming Zhao
3. Jing Lin
4. Mengjie Huang
5. Min Xia
6. Rui Yang
7. Zidong Wang
8. Roberto Ferrero

In Mechanical Engineering

1. Ming Zhao
2. Jing Lin
3. Min Xia
4. Kaixuan Liang
5. Jinyang Jiao
6. Haidong Shao

Citations in the corpus

• Deep Transfer Learning for Bearing Fault Diagnosis: A Systematic Review Since 2016 (2023, methodology): “machines [89], [90], the labeled fault data in laboratory machines are easy to collect and can be used to resolve fault diagnosis tasks in industrial machines.”
• Limited Data Rolling Bearing Fault Diagnosis With Few-Shot Learning (2019, background): “[32] proposed a feature-based transfer neural network (FTNN).”
• Intelligent Fault Diagnosis of Rotor-Bearing System Under Varying Working Conditions With Modified Transfer Convolutional Neural Network and Thermal Images (2020, background): “In engineering practice, the operating conditions of equipment frequently change, which will result in different distributions of the collected samples [23].”
• A comprehensive review on convolutional neural network in machine fault diagnosis (2020, background): “Yang et al. [212] combined the CNN with MMD to introduce a transfer learning network for the fault diagnosis from laboratory bearings to locomotive bearings.”