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Ball bearing vibration data for detecting and quantifying spall faults

Ismail, Mohamed AA and Windelberg, Jens and Bierig, Andreas and Bravo-Imaz, Iñaki and Arnaiz, Aitor (2023) Ball bearing vibration data for detecting and quantifying spall faults. Data in Brief. Elsevier. doi: 10.1016/j.dib.2023.109019. ISSN 2352-3409.

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Official URL: https://www.sciencedirect.com/science/article/pii/S2352340923001373?via%3Dihub

Abstract

Ball bearings are essential components of electromechanical systems, and their failures significantly affect the service lifetime of these systems. For highly reliable and safety-critical electromechanical systems in energy and aerospace sectors, early bearing fault detection and quantification are crucial. The vibration measurements of bearing fatigue faults, i.e., spalls, are typically induced by multiple excitation mechanisms depending on the fault size and the operating conditions. This data article contains vibration datasets for faulty ball bearings, including the common vibration excitation mechanisms for various fault sizes and operating conditions. These faults are artificially seeded on bearing races by a precise machining process to emulate realistic fatigue faults. This data article is beneficial for better understanding the vibration signal characteristics under different fault sizes and for validating condition monitoring methods for various industrial and aerospace applications.

Item URL in elib:https://elib.dlr.de/194103/
Document Type:Article
Title:Ball bearing vibration data for detecting and quantifying spall faults
Authors:
AuthorsInstitution or Email of AuthorsAuthor's ORCID iDORCID Put Code
Ismail, Mohamed AAUNSPECIFIEDhttps://orcid.org/0000-0002-2077-0681UNSPECIFIED
Windelberg, JensUNSPECIFIEDhttps://orcid.org/0000-0002-9249-2967UNSPECIFIED
Bierig, AndreasUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Bravo-Imaz, IñakiUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Arnaiz, AitorUNSPECIFIEDUNSPECIFIEDUNSPECIFIED
Date:1 March 2023
Journal or Publication Title:Data in Brief
Refereed publication:Yes
Open Access:Yes
Gold Open Access:Yes
In SCOPUS:Yes
In ISI Web of Science:Yes
DOI:10.1016/j.dib.2023.109019
Publisher:Elsevier
Series Name:Elsevier
ISSN:2352-3409
Status:Published
Keywords:Bearing defects condition monitoring health assessment predictive maintenance
HGF - Research field:Aeronautics, Space and Transport
HGF - Program:Aeronautics
HGF - Program Themes:Components and Systems
DLR - Research area:Aeronautics
DLR - Program:L CS - Components and Systems
DLR - Research theme (Project):L - Aircraft Systems
Location: Braunschweig
Institutes and Institutions:Institute of Flight Systems
Institute of Flight Systems > Safety Critical Systems&Systems Engineering
Deposited By: Ismail, Dr. Mohamed AA
Deposited On:25 Apr 2023 11:28
Last Modified:25 Jan 2024 14:07

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