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Research Papers

Cyclostationary Analysis of a Faulty Bearing in the Wind Turbine

[+] Author and Article Information
Zhiyong Ma, Dameng Wang

School of Energy, Power and
Mechanical Engineering,
North China Electric Power University,
Beijing 102206, China

Yibing Liu

School of Energy, Power and
Mechanical Engineering,
North China Electric Power University,
Beijing 102206, China;
Key Laboratory of Condition Monitoring and
Control for Power Plant Equipment
of Ministry of Education,
North China Electric Power University,
Beijing 102206, China

Wei Teng

School of Energy, Power and
Mechanical Engineering,
North China Electric Power University,
Beijing 102206, China;
Key Laboratory of Condition Monitoring and
Control for Power Plant Equipment of
Ministry of Education,
North China Electric Power University,
Beijing 102206, China
e-mail: tengw_ncepu@163.com

Andrew Kusiak

Mechanical and Industrial Engineering,
3131 Seamans Center,
The University of Iowa,
Iowa City, IA 52242-1527

1Corresponding author.

Contributed by the Solar Energy Division of ASME for publication in the JOURNAL OF SOLAR ENERGY ENGINEERING: INCLUDING WIND ENERGY AND BUILDING ENERGY CONSERVATION. Manuscript received December 11, 2016; final manuscript received January 13, 2017; published online February 8, 2017. Assoc. Editor: Yves Gagnon.

J. Sol. Energy Eng 139(3), 031006 (Feb 08, 2017) (12 pages) Paper No: SOL-16-1508; doi: 10.1115/1.4035846 History: Received December 11, 2016; Revised January 13, 2017

Bearing faults occur frequently in wind turbines, thus resulting in an unplanned downtime and economic loss. Vibration signal collected from a failing bearing exhibits modulation phenomenon and “cyclostationarity.” In this paper, the cyclostationary analysis is utilized to the vibration signal from the drive-end of the wind turbine generator. Fault features of the inner and outer race become visible in the frequency–cyclic frequency plane. Such fault signatures can not be produced by the traditional demodulation methods. Analysis results demonstrate effectiveness of the cyclostatonary analysis. The disassembled faulty bearing visualizes the fault.

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References

Figures

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Fig. 1

Structure of wind turbine drive train

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Fig. 2

Structure of ball bearings: (a) angular contact ball bearing and (b) deep groove ball bearing

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Fig. 7

Disassembled bearing at the drive-end of the generator

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Fig. 8

Time signal 6 months earlier: (a) time signal, (b) power spectrum, (c) envelope spectrum in the band 1000–3000 Hz, and (d) envelope spectrum in the band 5000–6500 Hz

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Fig. 6

Slice of CCF of the vibration signal at f = 3500 Hz

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Fig. 5

Slice of CCF of the vibration signal at f = 9000 Hz

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Fig. 9

Cyclic coherence function of the vibration signal 6 months earlier

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Fig. 10

Slice of CCF of the vibration signal 6 months earlier at f = 9000 Hz

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Fig. 11

Slice of CCF of the vibration signal 6 months earlier at f = 10,000 Hz

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Fig. 12

Decomposed modes of empirical wavelet transform using the vibration signal 6 months earlier

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Fig. 16

The slice of STFT between 2000 Hz and 2800 Hz at t = 1 s

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Fig. 13

The result produced by the continuous Morlet wavelet transform for the vibration signal 6 months earlier

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Fig. 15

Short time Fourier transform of the original signal 6 months earlier

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Fig. 14

Smoothed pseudo Wigner–Ville distribution of the filtered signal

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Fig. 4

Cyclic coherence function of the vibration signal

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Fig. 3

Time signal at the drive-end of a generator: (a) time signal, (b) power spectrum, (c) envelope spectrum in the band 1000–3000 Hz, and (d) envelope spectrum in the band 5000–6500 Hz

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