Bearing Fault Diagnosis Based on Wavelet Analysis

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Abstract:

The de-noising principle using wavelet was discussed and the de-noising property was compared with that of using general threshold strategy. The steps of the per-level de-noising method were then given and the experimental study with a vibration model was carried out. The results prove that the method is advantageous to de-noising, which is more suitable to recover the interest mutations signal buried in intensive background noise. The application of the method in the bearing vibration was presented and the results show that it can inhibit the background noise effectively and recover the interest information satisfactorily.

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Periodical:

Advanced Materials Research (Volumes 706-708)

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1763-1768

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June 2013

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© 2013 Trans Tech Publications Ltd. All Rights Reserved

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[1] ZHANG Xian WANG Hongli. Evolutionary Wavelet Denoising and Its Application to Ball Bearing Fault Diagnosis [J]. Journal of Mechanical Engineering.2010,46(15):76-81.

DOI: 10.3901/jme.2010.15.076

Google Scholar

[2] Qin Ping; Yan Bing; Li Hui. The Application of Wavelet Analysis in the Fault Diagnosis for the Plain Bearing s Contact Friction of Diesel Engine [J]. Chinese Internal Combustion Engine Engineering.2003, 24(3):56-60。

Google Scholar

[3] ZANG Yu-ping 1-3; ZHANG De-jiang 4; WANG Wei-zheng 3. Per-level threshold de-noising method using wavelet and its application in engine vibration analysis[J]. Journal of Vibration and Shock.2009, 28(8):56-60.

Google Scholar

[4] HE Bin; QI Jia-jie; LI Ming-he. Application and research of wavelet analysis in fault diagnosis of rolling bearings[J]. Journal of Zhejiang University(Engineering Science).2009, 43(7).

Google Scholar

[5] ZHANG Dejiang.MATLAB Wavelet analysis [M].Beijing:Machinery industry press,2009.

Google Scholar

[6] QU Weiwei GAO Feng. Study on Wavelet Threshold Denoising Algorithm Based on Estimation of Noise Variance [J]. Journal of Mechanical Engineering,2010,46(2):28-33.

DOI: 10.3901/jme.2010.02.028

Google Scholar

[7] Zhu Hongjun Wang Zhong. ACCURATE EXTRACTION FOR THE CHARACTERISTIC INFORMATION OF TRANSIENT SIGNAL WITH WAVELET TRANSFORMS [J]. Journal of Mechanical Engineering,2005,41(10):196-199.

DOI: 10.3901/jme.2005.12.196

Google Scholar

[8] LIU Shou-shan; YANG Chen-long; LI Ling; ZHOU Xiao-jun. Adaptive wavelet thresholding based ultrasonic signal denoising [J].Journal of Zhejiang University(Engineering Science),2007, 41(9):1557-1560.

Google Scholar

[9] DONOHO D L.De-Noising by Soft-Thresholding.IEEE Trans.Inform.Theory, 1995,41:613-627.

DOI: 10.1109/18.382009

Google Scholar

[10] WANG Hao, ZHANG Lai-bin,WANG Zhao-hui.Application of wavelet threshold de-noising in flue gas turbine signal analysis[J].Jounmal of Southwest Petroleum University:Science & Technology Edition,2009, 31(2):130-136.

Google Scholar

[11] Xiang Dongyang; Wu Zhengguo; Hu Wenbiao; Hou Xinguo. Improved Denoising Method Using the Correlation of Multiwavelet Coefficient [J].Journal of Vibration, Measurement & Diagnosis,2010,30(5):561-566.

Google Scholar

[12] Yuan Jiasheng ,Feng Zhihua. Fault Diagnosis Research Based on Correlation and Wavelet for Gears [J]. Transactions of the Chinese Society for Agricultural Machinery, 2007, 38(8):120-123.

Google Scholar

[13] ZANG Yuping1; 2; 3 ZHANG Dejiang4 WANG Weizheng3. Fault Diagnosis of Engine Abnormal Sound Based on Wavelet Transform Technique [J]. Journal of Mechanical Engineering,2009, 45(6):239-245.

DOI: 10.3901/jme.2009.06.239

Google Scholar