Journal Article

HUST bearing: a practical dataset for ball bearing fault diagnosis

Hong, Hoang Si, Thuan, Nguyen Duc

2023

Overview

Objectives The 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 description In 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.

• Publisher: BioMed Central,BioMed Central Ltd,Springer Nature B.V,BMC
• Subject: Automation / Ball bearings / Bearing fault / Biomedical and Life Sciences / Biomedicine / Data acquisition / Data entry / Data Note / Data Notes / Dataset / Datasets / Defects / Electric motors / Engineering research / Fault diagnosis / Fault location (Engineering) / Life Sciences / Machine Learning / Mechanical properties / Medicine/Public Health / Methods / Neural networks / Reproducibility of Results / Signal processing / Software / Vibration / Working Conditions