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HUST bearing: a practical dataset for ball bearing fault diagnosis.

ABSTRACT:

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.

SUBMITTER: Thuan ND

PROVIDER: S-EPMC10327369 | biostudies-literature | 2023 Jul

REPOSITORIES: biostudies-literature

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HUST bearing: a practical dataset for ball bearing fault diagnosis.

Thuan Nguyen Duc ND Hong Hoang Si HS

BMC research notes 20230706 1

<h4>Objectives</h4>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.<h4>Data description</h4>In this work, we introduce a practical datas ...[more]

PMID: 37415236

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