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美国辛辛那提大学 IMS 轴承数据集

1.简介

2.实验介绍

An AC motor, coupled by a rub belt, keeps the rotation speed constant. The four bearings are in the same shaft and are forced lubricated by a circulation system that regulates the flow and the temperature. It is announced on the provided “Readme Document for IMS Bearing Data” in the downloaded file, that the test was stopped when the accumulation of debris on a magnetic plug exceeded a certain level indicating the possibility of an impending failure. The four bearings are all of the same type. There are double range pillow blocks rolling elements bearing.

Three (3) data sets are included in the data packet (IMS-Rexnord Bearing Data.zip). Each data set describes a test-to-failure experiment. Each data set consists of individual files that are 1-second vibration signal snapshots recorded at specific intervals. Each file consists of 20,480 points with the sampling rate set at 20 kHz. The file name indicates when the data was collected. Each record (row) in the data file is a data point. Data collection was facilitated by NI DAQ Card 6062E. Larger intervals of time stamps (showed in file names) indicate resumption of the experiment in the next working day.

Table 1. Bearing characteristics

  • Rexnord ZA-2115 Characteristics
Parameter name imperial metric
Pitch diameter 2.815 inch 71.5mm
Rolling element diameter 0.331 inch 8.4mm
Number of rolling element per row 16 16
Contact angle 15.17° 15.17°
Static load 6000 lbs 26690 N

Set No. 1:

  • Recording Duration: October 22, 2003 12:06:24 to November 25, 2003 23:39:56
  • No. of Files: 2,156
  • No. of Channels: 8
  • Channel Arrangement: Bearing 1 – Ch 1&2; Bearing 2 – Ch 3&4; Bearing 3 – Ch 5&6; Bearing 4 – Ch 7&8.
  • File Recording Interval: Every 10 minutes (except the first 43 files were taken every 5 minutes)
  • File Format: ASCII
  • Description: At the end of the test-to-failure experiment, inner race defect occurred in bearing 3 and roller element defect in bearing 4.

Set No. 2:

  • Recording Duration: February 12, 2004 10:32:39 to February 19, 2004 06:22:39
  • No. of Files: 984
  • No. of Channels: 4
  • Channel Arrangement: Bearing 1 – Ch 1; Bearing2 – Ch 2; Bearing3 – Ch3; Bearing 4 – Ch 4.
  • File Recording Interval: Every 10 minutes
  • File Format: ASCII
  • Description: At the end of the test-to-failure experiment, outer race failure occurred in bearing 1.

Set No. 3

  • Recording Duration: March 4, 2004 09:27:46 to April 4, 2004 19:01:57
  • No. of Files: 4,448
  • No. of Channels: 4
  • Channel Arrangement: Bearing1 – Ch 1; Bearing2 – Ch 2; Bearing3 – Ch3; Bearing4 – Ch4;
  • File Recording Interval: Every 10 minutes
  • File Format: ASCII
  • Description: At the end of the test-to-failure experiment, outer race failure occurred in bearing 3.

Table 2. Datasets description

Number of files Number of channels Endurance duration Duration of recorded signal Announced damages at the end of the endurance
Dataset 1 2156 8 49680 min 34 days 12h 36 min Bearing 3: inner race Bearing 4: rolling element
Dataset 2 984 4 9840 min 6 days 20h 16 min Bearing 1: outer race
Dataset 3 4448 4 44480 min 31 days 10h 74 min Bearing 3: outer race

Table 3. Characteristic frequencies of the test rig

Characteristic frequencies
Shaft frequency 33.3 Hz
Ball Pass Frequency Outer race (BPFO) 236 Hz
Ball Pass Frequency Inner race (BPFI) 297 Hz
Ball Spin Frequency (BSF) 278Hz (2x139 Hz)
Fundamental Train Frequency (FTF) 15 Hz

3.使用情况

  • Gousseau W, Antoni J, Girardin F, et al. Analysis of the Rolling Element Bearing data set of the Center for Intelligent Maintenance Systems of the University of Cincinnati: CM2016, 2016[C].

  • Qiu H, Lee J, Lin J, et al. Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics[J]. Journal of Sound and Vibration, 2006,289(4):1066-1090.

  • A data-driven failure prognostics method based on mixture of Gaussians hidden Markov models, Tobon-Mejia, Diego Alejandro and Medjaher, Kamal and Zerhouni, Noureddine and Tripot, Gerard, Reliability, IEEE Transactions on, Vol. 61 No. 2, 491--503, 2012

  • Health condition monitoring of machines based on hidden markov model and contribution analysis, Yu, Jianbo, Instrumentation and Measurement, IEEE Transactions on, Vol. 61 No. 8, 2200--2211, 2012

  • Local and nonlocal preserving projection for bearing defect classification and performance assessment, Yu, Jianbo, Industrial Electronics, IEEE Transactions on, Vol. 59 No. 5, 2363--2376, 2012

  • Major Challenges in Prognostics: Study on Benchmarking Prognostics Datasets, Eker, OF and Camci, F and Jennions, IK, European Conference of Prognostics and Health Management Society, 2012

  • Remaining useful life estimation for systems with non-trendability behaviour, Porotsky, Sergey and Bluvband, Zigmund, Prognostics and Health Management (PHM), 2012 IEEE Conference on, 1--6, 2012

  • Logical analysis of maintenance and performance data of physical assets, ID34, Yacout, S, Reliability and Maintainability Symposium (RAMS), 2012 Proceedings-Annual, 1--6, 2012

  • Power wind mill fault detection via one-class $\nu$-SVM vibration signal analysis, Martinez-Rego, David and Fontenla-Romero, Oscar and Alonso-Betanzos, Amparo, Neural Networks (IJCNN), The 2011 International Joint Conference on, 511--518, 2011

  • cbmLAD-using Logical Analysis of Data in Condition Based Maintenance, Mortada, M-A and Yacout, Soumaya, Computer Research and Development (ICCRD), 2011 3rd International Conference on, 30--34, 2011

  • Hidden Markov Models for failure diagnostic and prognostic, Tobon-Mejia, DA and Medjaher, Kamal and Zerhouni, Noureddine and Tripot, G{'e}rard, Prognostics and System Health Management Conference (PHM-Shenzhen), 2011, 1--8, 2011

  • Application of Wavelet Packet Sample Entropy in the Forecast of Rolling Element Bearing Fault Trend, Wang, Fengtao and Zhang, Yangyang and Zhang, Bin and Su, Wensheng, Multimedia and Signal Processing (CMSP), 2011 International Conference on, 12--16, 2011

  • A Mixture of Gaussians Hidden Markov Model for failure diagnostic and prognostic, Tobon-Mejia, Diego Alejandro and Medjaher, Kamal and Zerhouni, Noureddine and Tripot, Gerard, Automation Science and Engineering (CASE), 2010 IEEE Conference on, 338--343, 2010

  • Wavelet filter-based weak signature detection method and its application on rolling element bearing prognostics, Qiu, Hai and Lee, Jay and Lin, Jing and Yu, Gang, Journal of Sound and Vibration, Vol. 289 No. 4, 1066--1090, 2006

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