Your Bearing Fault Diagnosis Based on Bispectrum and Bispectrum Entropy Feature

Abstract:

Fault feature extraction and application is the key technology of fault diagnosis. In this paper, a fault diagnosis method using bispectrum and bispectrum entropy as the fault feature parameters is put forward. Bispectrum entropy as the information entropy in bispectrum domain can reflect the complexity of information energy. When the structure is failed, the distribution of bispectrum will be changed. bispectrum entropy can reflect this change and achieve good separation of the different types of fault. Vibration signal in different bearing states of a secondary drive gearbox is compared and analyzed, bispectrum energy spetrum and bispectrum entropy are extracted. Feature vector is set up via bispectrum entropy for the fault pattern recognition and diagnosis by BP neural network. The analysis result proves that bispectrum entropy is more sensitive to fault characteristic and can separate the fault of bearing.

• Periodical: Advanced Materials Research (Volume 159)
• Pages: 708-713
• DOI: https://doi.org/10.4028/www.scientific.net/AMR.159.708
• Online since: December 2010
• Authors: Jin Ying Huang, Hong Xia Pan, Shi Hua Bi
• Keywords: Bearing, Bispectrum, Entropy, Fault Diagnosis

References

[1] SAADAOUI Wajdi, JELASSI Khaled, Gearbox-Induction machine Bearing fault diagnosis using spectral analysis, Second UKSIM European Symposium on Computer Modeling and Simulation, pp.347-352, (2008).

DOI: 10.1109/ems.2008.85

[2] Yang Jiangtian, Chen Jiaji, Zeng Ziping, Extracting Fault Features Using Higher Order Spectra for Rotating Machinery, Journal of Vibration Engineering, No. 1, pp.13-17, (2001).

[3] LI Yang-huan，GAO Feng，LI Teng，ZHOU Zhi-min, Novel method for feature selection based on entropy. Computer Engineering and Applications, Computer Engineering and Applications, vol. 45, No. 15, pp.54-57, (2009).

[4] Berthold Bein, Entropy, Best Practice & Research Clinical Anaesthesiology, vol. 20, No. 1, pp.101-109, (2006).

DOI: 10.1016/j.bpa.2005.07.009

[5] L.A. Overbey, M.D. Todd, Effects of noise on transfer entropy estimation for damage detection, Mechanical Systems and Signal Processing, vol. 23, p.2178–2191, (2009).

DOI: 10.1016/j.ymssp.2009.03.016

[6] Bing Li, Peilin Zhang, etc, Feature Extraction and Selection for Fault Diagnosis of Gear Using Wavelet Entropy and Mutual Information, ICSP2008 Proceedings, pp.2846-2850, (2008).

DOI: 10.1109/icosp.2008.4697740

[7] Geng Junbao, Huang Shuhong, etc, Rotating machinery fault diagnosis based on close degree to information entropy, J . Huazhong Univ. of Sci. & Tech. (Nature Science Edition), vol. 34, No. 11, pp.93-95, (2006).

[8] Dejie Yu, Yu Yang, Junsheng Cheng, Application of time–frequency entropy method based on Hilbert–Huang transform to gear fault diagnosis, Measurement, vol. 40, p.823–830, (2007).

DOI: 10.1016/j.measurement.2007.03.004

[9] LI Xue-yao, ZOU Xiao-jie, etc, "Research on empirical mode decomposition based on spectrum entropy methods and principal component analysis. Journal of Harbin Engineering University, vol. 30, No. 7, pp.797-803, (2009).

[10] Yu Dejie, Zhang Wei, etc, Application of Time-frequency Entropy to Gear Fault Diagnosis Based on EMD, JOURNAL OF VIBRATION AND SHOCK, vol. 24, No. 5, pp.26-27, (2005).

[11] Nadia Mammone, Francesco Carlo Morabito, Enhanced automatic artifact detection based on independent component analysis and Renyi's entropy, Neural Networks, vol. 21, p.1029–1040, (2008).

DOI: 10.1016/j.neunet.2007.09.020

[12] Chen Jin, Jiang Ming, The State-of-Art of the Application of the Higher-Order Cyclostationary Statistics in Mechanical Fault Diagnosis, Journal of Vibration Engineering, No. 2, pp.126-134, (2001).

[13] Wang Huamin, Chen Xia, An Gang, Fan Xinhai, Fault Diagnosis of Gearbox Based on Higher Order Cumulants, Journal of Mechanical Strength, No. 3, pp.247-249, (2004).

[14] Shen Fenglin, Chen He'an, Biomedical signal processing, University of Science and Technology of China Press, 1999, pp.320-355.