Intermittent Fault Diagnosis under Extreme Vibration Environment Based on EMD and Neural Network

Hui Zhou; Guan Jun Liu; Jing Qiu; Ke Hong Lv

doi:10.4028/www.scientific.net/KEM.584.97

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Dynamic Analysis and Various Mode Strategy Comparisons of the Two-Mode Hybrid Transmission
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HomeKey Engineering MaterialsKey Engineering Materials Vol. 584Intermittent Fault Diagnosis under Extreme...

Intermittent Fault Diagnosis under Extreme Vibration Environment Based on EMD and Neural Network

Abstract:

ntermittent fault under extreme vibration environment is an important cause to affect the function of mechatronics system. A new intermittent fault diagnostic technique based on EMD and neural network methods is proposed in this paper. Firstly, the essential characteristics of intermittent fault under extreme vibration environment are summarized. Then in view of the extreme vibration analysis of electromechanical systems, a new intermittent fault diagnostic technique is put forward by combining EMD and neural network approach. Lastly, the technique is verified by conducting in the heading attitude system of helicopter.

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Periodical:

Key Engineering Materials (Volume 584)

Pages:

97-101

DOI:

https://doi.org/10.4028/www.scientific.net/KEM.584.97

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Online since:

September 2013

Authors:

Hui Zhou, Guan Jun Liu, Jing Qiu, Ke Hong Lv

Keywords:

Empirical Mode Decomposition (EMD), Extreme Vibration Environment, Intermittent Fault Diagnosis, Neural Network (NN)

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References

[1] L. Suarz, P.J. Zarco-Tejada, V. Gonzalez-Dugo. Detecting water stress effects on fruit quality in orchards with time-series PRI [R]. Remote Sensing of Environment. 114 (2010) 286-298.

DOI: 10.1016/j.rse.2009.09.006

Google Scholar

[2] H. Yang, B. Jiang, Y. Zhang. Tolerance of intermittent faults in spacecraft attitude control: switched system approach. [R]. the institution of Engineering and Technology. 2012. 2049-(2056).

DOI: 10.1049/iet-cta.2011.0453

Google Scholar

[3] Bryan Steadman, Floyd Berghout, Nathan Olsen. Intermittent Fault Detection and Isolation System [R] IEEE AUTOTESTCON (2011).

DOI: 10.1109/autest.2008.4662580

Google Scholar

[4] Xie Zheng. Applied Mathematics [M]. Beijing: Science Press. (2007).

Google Scholar

[5] Wen Xisen. Pattern Recognition and Status Monitor [M]. Beijing: Science Press. (2007).

Google Scholar

[6] Zhu Fulei, Peng Zhike, Feng Zhipeng, Li Zhinong. Introduction to Machine Fault Diagnosis and Modern Methods of Signal Processing [M]. Beijing: Science Press. (2007).

Google Scholar

[7] Zhong Bingren, Huang Ren. Introduction to Machine Fault Diagnosis [M]. Beijing: Machinery Industry Press. (2002).

Google Scholar

[8] Qu Liangsheng, He Zhengjia. Introduction to Machine Fault Diagnosis [M]. Shanghai: Shanghai Science and Technology Press.

Google Scholar