Reach on Micro-Motor Acoustics Fault Diagnosis Based on Loose Wavelet Neural Network

Ying Ge Li; Gui Tang Wang; Zheng Li; Xin Liang Yin

doi:10.4028/www.scientific.net/KEM.579-580.775

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HomeKey Engineering MaterialsKey Engineering Materials Vols. 579-580Reach on Micro-Motor Acoustics Fault Diagnosis...

Reach on Micro-Motor Acoustics Fault Diagnosis Based on Loose Wavelet Neural Network

Abstract:

Nicro-motor voice signal contains abundant running status information as well as vibration signal, aiming at the problem that it is difficult to obtain vibration signal in the production line of micro-motor, this paper proposes a micro-motor acoustic fault diagnosis methods based on loose wavelet neural network. Wavelet packet decomposition and reconstruction algorithm is utilized to extract micro-motor voice signals in each frequency band energy as the characteristic parameters of fault characteristic parameter samples will input to improve the BP neural network for training, build up the fault type of classifier, the realization of fault intelligent diagnosis. Application results show that a reasonable design of neural network has strong ability of fault identification; use loose micro-motor acoustic wavelet neural network fault diagnosis is feasible.

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Periodical:

Key Engineering Materials (Volumes 579-580)

Pages:

775-780

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.579-580.775

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Online since:

September 2013

Authors:

Ying Ge Li, Gui Tang Wang, Zheng Li, Xin Liang Yin

Keywords:

Acoustic, BP Neural Network (BPNN), Micro-Motor, Wavelet Analysis

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References

[1] Z.D. Li, X.R. Li and H.J. Yu: Fault Diagnosis Method of Acoustic (First Edition ) (Metallurgical Industry Press, Beijing 1989) (In Chinese).

Google Scholar

[2] U. Benko, J. Petrovcic and D. Juricic: Journal of Sound and Vibration, Vol. 276 (2004) No. 3-5, pp.781-806.

Google Scholar

[3] B. Chen, Z.L. Yan and X. B Cheng: Beijing University of Posts and Telecommunications, Vol. 34 (2011) No. 4, pp.71-74. (In Chinese).

Google Scholar

[4] J. Zhang and S. Lu: Agricultural Machinery, Vol. 38 (2007) No. 10, pp.178-182. (In Chinese).

Google Scholar

[5] X.H. He and R. Shi: Computer and Modernization, 2009, 7 pp.17-21 (In Chinese).

Google Scholar

[6] C. Zhe, and T.J. Feng: Qingdao Ocean University (Natural Science Edition), Vol. 29 (1999) No. 4, pp.663-668.

Google Scholar

[7] H.F. Tang: Network Fault Diagnosis System Based on BP Neural Network. (Ph.D., Nanjing University, China 2005).

Google Scholar