Abstract
To diagnose the bearing faults, both local mean mode decomposition and probabilistic neural networks were proposed to use. A new criterion for the sifting process to stop was given to make this method more efficient. Calculated the average of instantaneous frequency and the ratio of energy of the decomposed parts by local mean mode decomposition, and used the probabilistic neural networks to classify it. The results indicated that this method is precise and valid. Therefore, it develops the intelligent fault diagnosis.
Access this chapter
Tax calculation will be finalised at checkout
Purchases are for personal use only
Preview
Unable to display preview. Download preview PDF.
References
Sheng Zhaoshun, Yin Yiling. Equipment monitoring and fault diagnosis technology and application. Beijing: Chemical Industry Press, 2003: 224–233.
Gai Qiang, MA Xiaojiang, Z Haiyong, etc. Processing time-varying signals by a new method. 2001 CIE International Conference on Radar Proceedings, Beijing, China, 2001: 1011–1014.
Huang N E, Shen Z, Long S R, etc. The empirical mode decomposition and the Hilbert spectrum for nonlinear nonstationary time series analysis. Proc. Royal Society, London series, 1998, A454: 903–995.
Gai Qiang, Zhang Haiyong, Xu Xiaogang. Sdudy of Frequency Multiresolution Analysis of the Hilbert-Huang Transform. Acta Electronica Sinica, 2005, 33(3):563–566.
Xu Yonggang. Application research on new methods in time domain for monitoring and diagnosis of electric mechanical equipment. Xian: Xi’an Jiaotong University, 2003.
Zhu Fulei, Peng Zhike, Feng Zhipeng, etc. Mechanical fault diagnosis of modern signal processing methods. Beijing: Science Press, 2009:1–130.
Zhou Yunlong, Song Yanhong, Chen Jun. Research on Rolling Element Bearings Fault Diagnosis Based on HHT and GA-BP Neural Network. Machine Tool& Hydrauucs, 2010, 38(17): 133–137.
J. T. Tom and R. C. Gouzales . “Pattern Recognition Principles”, Addison-Wesley Pub. Co., August 1977.
Zhang Yunpeng, Gai Qiang, Zhou Yang, etc. Research on the Parzen window based probabilistic neural networks and fault diagnosis by it, Journal of Dalian Naval Academy, 2010, 33(5): 84–86.
Author information
Authors and Affiliations
Corresponding author
Editor information
Editors and Affiliations
Rights and permissions
Copyright information
© 2012 Springer-Verlag London Limited
About this paper
Cite this paper
Yunpeng, Z., Qiang, G., Yang, Z. (2012). Application of Local Mean Mode Decomposition in Bearing Fault Diagnosis. In: Zhu, R., Ma, Y. (eds) Information Engineering and Applications. Lecture Notes in Electrical Engineering, vol 154. Springer, London. https://doi.org/10.1007/978-1-4471-2386-6_42
Download citation
DOI: https://doi.org/10.1007/978-1-4471-2386-6_42
Publisher Name: Springer, London
Print ISBN: 978-1-4471-2385-9
Online ISBN: 978-1-4471-2386-6
eBook Packages: EngineeringEngineering (R0)