University of Ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets
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 Vibrat...
| Main Authors: | , , |
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| Format: | Article |
| Language: | English |
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Elsevier
2023-08-01
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| Series: | Data in Brief |
| Subjects: | |
| Online Access: | http://www.sciencedirect.com/science/article/pii/S2352340923004456 |
| _version_ | 1827866521783762944 |
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| author | Mert Sehri Patrick Dumond Michel Bouchard |
| author_facet | Mert Sehri Patrick Dumond Michel Bouchard |
| author_sort | Mert Sehri |
| collection | DOAJ |
| 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. |
| first_indexed | 2024-03-12T15:04:54Z |
| format | Article |
| id | doaj.art-f12aa090ddfc49dbbd19b77e07412b89 |
| institution | Directory Open Access Journal |
| issn | 2352-3409 |
| language | English |
| last_indexed | 2024-03-12T15:04:54Z |
| publishDate | 2023-08-01 |
| publisher | Elsevier |
| record_format | Article |
| series | Data in Brief |
| spelling | doaj.art-f12aa090ddfc49dbbd19b77e07412b892023-08-13T04:53:57ZengElsevierData in Brief2352-34092023-08-0149109327University of Ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasetsMert Sehri0Patrick Dumond1Michel Bouchard2Department of Mechanical Engineering, University of Ottawa, 161 Louis Pasteur, Ottawa, Ontario, Canada; Corresponding author.Department of Mechanical Engineering, University of Ottawa, 161 Louis Pasteur, Ottawa, Ontario, CanadaGeneral Bearing Service Inc., 490 Kent Street, Ottawa, Ontario, CanadaThe 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.http://www.sciencedirect.com/science/article/pii/S2352340923004456VibrationMachine condition monitoringFault detection/DiagnosisSignal processing |
| spellingShingle | Mert Sehri Patrick Dumond Michel Bouchard University of Ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets Data in Brief Vibration Machine condition monitoring Fault detection/Diagnosis Signal processing |
| title | University of Ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets |
| title_full | University of Ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets |
| title_fullStr | University of Ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets |
| title_full_unstemmed | University of Ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets |
| title_short | University of Ottawa constant load and speed rolling-element bearing vibration and acoustic fault signature datasets |
| title_sort | university of ottawa constant load and speed rolling element bearing vibration and acoustic fault signature datasets |
| topic | Vibration Machine condition monitoring Fault detection/Diagnosis Signal processing |
| url | http://www.sciencedirect.com/science/article/pii/S2352340923004456 |
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