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

Journal: BMC research notes
PMID: 37415236

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.

Authors

  • Nguyen Duc Thuan
    School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, No. 1 Dai Co Viet Road, Hanoi, Vietnam.
  • Hoang Si Hong
    School of Electrical and Electronic Engineering, Hanoi University of Science and Technology, No. 1 Dai Co Viet Road, Hanoi, Vietnam. hong.hoangsy@hust.edu.vn.

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