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MFPT 轴承数据集说明

1.概述

NRG Systems总工程师Eric Bechhoefer博士代表MFPT组装和准备数据。

2.试验说明

The test rig was equipped with a NICE bearing with the following parameters:

  • Roller diameter: rd = 0.235
  • Pitch diameter: pd = 1.245
  • Number of elements: ne = 8
  • Contact angle: ca = 0

The data set1,2 comprises the following, and can be downloaded as a zip file package he:Fault Data Sets

  • 3 baseline conditions: 270 lbs of load, input shaft rate of 25 Hz, sample rate of 97,656 sps, for 6 seconds

  • 3 outer race fault conditions: 270 lbs of load, input shaft rate of 25 Hz, sample rate of 97,656 sps for 6 seconds

  • 7 outer race fault conditions: 25, 50, 100, 150, 200, 250 and 300 lbs of load, input shaft rate 25 Hz, sample rate of 48,828 sps for 3 seconds (bearing resonance was found be less than 20 kHz)

  • 7 inner race fault conditions: 0, 50, 100, 150, 200, 250 and 300 lbs of load, input shaft rate of 25 Hz, sample rate of 48,828 sps for 3 seconds

  • 5 data analysis (.m) files that relate to Eric Bechhoefer’s introductory paper referred to below

  • Three2 real world example files are also included: an intermediate shaft bearing from a wind turbine (data structure holds bearing rates and shaft rate), an oil pump shaft bearing from a wind turbine, and a real world planet bearing fault).

  • Note1: The data is stored in a Matlab® double-precision, binary format *.mat file. The data structure holds the load, shaft rate, sample rate and a vector of “g” data.

  • Note2: The initial data uploaded to the website in October 2012 included errors, in that the sample rate was defined as 50 Hz, when in fact it was 25 Hz. New data sets correcting this error were uploaded on 27 Feb 13. In addition, a third real world example was added]

3.使用情况

  • Lee D, Siu V, Cruz R, et al. Convolutional neural net and bearing fault analysis[C]//Proceedings of the International Conference on Data Mining (DMIN). The Steering Committee of The World Congress in Computer Science, Computer Engineering and Applied Computing (WorldComp), 2016: 194.论文链接

  • Lessmeier C, Kimotho J K, Zimmer D, et al. Condition monitoring of bearing damage in electromechanical drive systems by using motor current signals of electric motors: A benchmark data set for data-driven classification[C]//Proceedings of the European conference of the prognostics and health management society. 2016: 05-08.07.论文链接
    利用电机电流信号监测机电传动系统轴承损坏情况:数据驱动分类的基准数据集
    对CWRU: Bearing Data Center/ Seeded Fault Test Data，FEMTO Bearing Data Set，MFPT Fault Data Sets，Bearing Data Set IMS四个数据集进行了分析和介绍。

  • Verstraete D, Ferrada A, Droguett E L, et al. Deep learning enabled fault diagnosis using time-frequency image analysis of rolling element bearings[J]. Shock and Vibration, 2017, 2017.论文链接 通过对滚动轴承的时频图像分析深度学习实现了故障诊断

  • Yu H, Wang K, Li Y. Multiscale Representations Fusion With Joint Multiple Reconstructions Autoencoder for Intelligent Fault Diagnosis[J]. IEEE Signal Processing Letters, 2018, 25(12): 1880-1884.论文链接

  • Sobie C, Freitas C, Nicolai M. Simulation-driven machine learning: Bearing fault classification[J]. Mechanical Systems and Signal Processing, 2018, 99: 403-419. 论文链接

  • Li H, Zhao J, Liu J, et al. Application of empirical mode decomposition and Euclidean distance technique for feature selection and fault diagnosis of planetary gearbox[J]. Journal of Vibroengineering, 2016, 18(8).论文链接

  • Barbini L, Ompusunggu A P, Hillis A J, et al. Phase editing as a signal pre-processing step for automated bearing fault detection[J]. Mechanical Systems and Signal Processing, 2017, 91: 407-421.论文链接

4.数据特点

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