Bearing fault detection based on area equalization

Meng Wang; Jiong Yu; Hongyong Leng; Xusheng Du; Yiran Liu

doi:10.1117/12.3055676

12 December 2024 Bearing fault detection based on area equalization

Meng Wang, Jiong Yu, Hongyong Leng, Xusheng Du, Yiran Liu

Proceedings Volume 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024); 134390W (2024) https://doi.org/10.1117/12.3055676
Event: Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 2024, Xiamen, China

Abstract

The importance of detecting bearing failures lies in ensuring the reliability, safety, and efficiency of mechanical systems. A failed or damaged bearing can lead to severe mechanical breakdowns, injuries, and equipment damage. However, current bearing fault diagnosis algorithms lack accuracy and efficiency. To address these issues, we propose a bearing fault diagnosis method called Bearing Fault Detection based on Area Equalization (BFAE) algorithm, which incorporates outlier detection algorithms from machine learning. The algorithm divides the dataset uniformly, identifies the optimal measurement radius, establishes a neighborhood for each object based on this radius, and evaluates the consistency of object density with the neighborhood’s average density. Subsequently, outliers are labeled, with objects exhibiting higher outliers classified as anomalous. A comparative analysis of the BFAE algorithm against five other algorithms (LOF, COF, Autoencoder) on ten high-dimensional real datasets demonstrates that the BFAE algorithm outperforms others in key metrics such as Area Under the Curve (AUC) and Accuracy (ACC). Bearing fault detection.

(2024) Published by SPIE. Downloading of the abstract is permitted for personal use only.

Citation Download Citation

Meng Wang, Jiong Yu, Hongyong Leng, Xusheng Du, and Yiran Liu "Bearing fault detection based on area equalization", Proc. SPIE 13439, Fourth International Conference on Testing Technology and Automation Engineering (TTAE 2024), 134390W (12 December 2024); https://doi.org/10.1117/12.3055676

ACCESS THE FULL ARTICLE

INSTITUTIONAL
Select your institution to access the SPIE Digital Library.

SELECT YOUR INSTITUTION

PERSONAL
Sign in with your SPIE account to access your personal subscriptions or to use specific features such as save to my library, sign up for alerts, save searches, etc.

PERSONAL SIGN IN

No SPIE Account? Create one

PURCHASE THIS CONTENT

SUBSCRIBE TO DIGITAL LIBRARY

50 downloads per 1-year subscription

Members: $195

Non-members: $335 ADD TO CART

25 downloads per 1 - year subscription

Members: $145

Non-members: $250 ADD TO CART

PURCHASE SINGLE ARTICLE

Includes PDF, HTML & Video, when available