Sehri Mert, Dumond Patrick, Bouchard Michel
Department of Mechanical Engineering, University of Ottawa, 161 Louis Pasteur, Ottawa, Ontario, Canada.
General Bearing Service Inc., 490 Kent Street, Ottawa, Ontario, Canada.
Data Brief. 2023 Jun 18;49:109327. doi: 10.1016/j.dib.2023.109327. eCollection 2023 Aug.
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.
数据的收集和分析在检测和诊断轴承故障中起着关键作用。然而，用于故障诊断的大型开放获取滚动轴承数据集的可用性有限。为了克服这一挑战，引入了渥太华大学在恒定负载和速度条件下运行的滚动轴承振动和声学故障特征数据集，以提供补充数据，这些数据可以与现有的轴承数据集合并或融合，以增加研究人员可用的数据量。该数据利用各种传感器，如加速度计、麦克风、称重传感器、霍尔效应传感器和热电偶，来收集有关轴承健康状况的高质量数据。通过纳入振动和声学信号，这些数据集支持传统的和基于机器学习的滚动轴承故障诊断方法。此外，该数据集为恒定负载下轴承寿命的加速劣化提供了有价值的见解,使其成为该领域研究的宝贵资源。最终，这些数据集为滚动轴承故障的检测和诊断提供了高质量的数据，从而对机械运行和维护具有重要意义。
