Academic Journal
University of Ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets
Title: | University of Ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets |
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Authors: | Mert Sehri, Patrick Dumond, Michel Bouchard |
Source: | Data in Brief, Vol 49, Iss , Pp 109327- (2023) |
Subject Terms: | Vibration, Machine condition monitoring, Fault detection/Diagnosis, Signal processing, Computer applications to medicine. Medical informatics, R858-859.7, Science (General), Q1-390 |
Publisher Information: | Elsevier, 2023. |
Publication Year: | 2023 |
Collection: | LCC:Computer applications to medicine. Medical informatics LCC:Science (General) |
Description: | The collection and analysis of data play a critical role in detecting and diagnosing faults in bearings. However, the availability of large open-access rolling-element bearing datasets for fault diagnosis is limited. To overcome this challenge, the University of Ottawa Rolling-element Bearing Vibration and Acoustic Fault Signature Datasets Operating under Constant Load and Speed Conditions are introduced to provide supplementary data that can be combined or merged with existing bearing datasets to increase the amount of data available to researchers. This data utilizes various sensors such as an accelerometer, a microphone, a load cell, a hall effect sensor, and thermocouples to gather quality data on bearing health. By incorporating vibration and acoustic signals, the datasets enable both traditional and machine learning-based approaches for rolling-element bearing fault diagnosis. Furthermore, this dataset offers valuable insights into the accelerated deterioration of bearing life under constant loads, making it an invaluable resource for research in this domain. Ultimately, these datasets deliver high quality data for the detection and diagnosis of faults in rolling-element bearings, thereby holding significant implications for machinery operation and maintenance. |
Document Type: | article |
File Description: | electronic resource |
Language: | English |
ISSN: | 2352-3409 |
Relation: | http://www.sciencedirect.com/science/article/pii/S2352340923004456; https://doaj.org/toc/2352-3409 |
DOI: | 10.1016/j.dib.2023.109327 |
Access URL: | https://doaj.org/article/f12aa090ddfc49dbbd19b77e07412b89 |
Accession Number: | edsdoj.f12aa090ddfc49dbbd19b77e07412b89 |
ISSN: | 23523409 |
DOI: | 10.1016/j.dib.2023.109327 |
Database: | Directory of Open Access Journals |