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Volume 4 Issue 4
Oct.  2017

IEEE/CAA Journal of Automatica Sinica

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Junbo Long, Haibin Wang, Peng Li and Hongshe Fan, "Applications of Fractional Lower Order Time-frequency Representation to Machine Bearing Fault Diagnosis," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 734-750, Oct. 2017. doi: 10.1109/JAS.2016.7510190
Citation: Junbo Long, Haibin Wang, Peng Li and Hongshe Fan, "Applications of Fractional Lower Order Time-frequency Representation to Machine Bearing Fault Diagnosis," IEEE/CAA J. Autom. Sinica, vol. 4, no. 4, pp. 734-750, Oct. 2017. doi: 10.1109/JAS.2016.7510190

Applications of Fractional Lower Order Time-frequency Representation to Machine Bearing Fault Diagnosis

doi: 10.1109/JAS.2016.7510190
Funds:

the National Natural Science Foundation of China 61261046

the National Natural Science Foundation of China 61362038

the Natural Science Foundation of Jiangxi Province 20142BAB207006

the Natural Science Foundation of Jiangxi Province 20151BAB207013

the Science and Technology Project of Provincial Education Department of Jiangxi Province GJJ14738

the Science and Technology Project of Provincial Education Department of Jiangxi Province GJJ14739

the Research Foundation of Health Department of Jiangxi Province 20175561

the Science and Technology Project of Jiujiang University 2016KJ001

the Science and Technology Project of Jiujiang University 2016KJ002

More Information
  • The machinery fault signal is a typical non-Gaussian and non-stationary process. The fault signal can be described by SαS distribution model because of the presence of impulses. Time-frequency distribution is a useful tool to extract helpful information of the machinery fault signal. Various fractional lower order (FLO) time-frequency distribution methods have been proposed based on fractional lower order statistics, which include fractional lower order short time Fourier transform (FLO-STFT), fractional lower order Wigner-Ville distributions (FLO-WVDs), fractional lower order Cohen class time-frequency distributions (FLO-CDs), fractional lower order adaptive kernel time-frequency distributions (FLO-AKDs) and adaptive fractional lower order time-frequency auto-regressive moving average (FLO-TFARMA) model time-frequency representation method. The methods and the exiting methods based on second order statistics in SαS distribution environments are compared, simulation results show that the new methods have better performances than the existing methods. The advantages and disadvantages of the improved time-frequency methods have been summarized. Last, the new methods are applied to analyze the outer race fault signals, the results illustrate their good performances.

     

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