HUST bearing: a practical dataset for ball bearing fault diagnosis

BMC Research Notes

Data Note

Hoang Si Hong¹  Nguyen Duc Thuan¹ 

[1] School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, No. 1 Dai Co Viet Road, Hanoi, Vietnam;

关键词: Dataset;    Bearing fault;    Fault diagnosis;   

DOI  :  10.1186/s13104-023-06400-4

 received in 2023-03-14, accepted in 2023-06-18,  发布年份 2023

来源: Springer

【 摘 要 】

ObjectivesThe rapid growth of machine learning methods has led to an increase in the demand for data. For bearing fault diagnosis, the data acquisition is time-consuming with complicated processes. Existing datasets are only focused on only one type of bearing, which limits real-world applications. Therefore, the objective of this work is to propose a diverse dataset for ball bearing fault diagnosis based on vibration.Data descriptionIn this work, we introduce a practical dataset named HUST bearing, which provides a large set of vibration data on different ball bearings. This dataset contains 99 raw vibration signals of 6 types of defects (inner crack, outer crack, ball crack, and their 2-combinations) on 5 types of bearing (6204, 6205, 6206, 6207, and 6208) at 3 working conditions (0 W, 200 W, and 400 W). Each vibration signal is sampled at a rate of 51,200 samples per second for 10 s. The data acquisition system is elaborately designed with high reliability.

【 授权许可】

CC BY   
© The Author(s) 2023