Research of Fault Diagnosis Method Based on Improved Extreme Learning Machine

Wen Bo Na; Qing Feng Jiang; Zhi Wei Su

doi:10.4028/www.scientific.net/AMM.727-728.872

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HomeApplied Mechanics and MaterialsApplied Mechanics and Materials Vols. 727-728Research of Fault Diagnosis Method Based on...

Research of Fault Diagnosis Method Based on Improved Extreme Learning Machine

Abstract:

In order to improve the accuracy of diagnosis pumping, and accelerate the speed of diagnosis, a fault diagnosis model based on improved extreme learning machine (RWELM) was proposed. Firstly, it extracted the energy characteristic eigenvector of dynamometer cards of an oilfield in northern Shanxi by using wavelet packet decomposition method. Then through simulation of fault diagnosis, and compare with the extreme learning machine (ELM), RBF neural networks and support vector machine (SVM). The experimental results show that the accuracy and the speed of fault diagnosis based on the RWELM are better than the ELM, RBF neural network and SVM.

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Periodical:

Applied Mechanics and Materials (Volumes 727-728)

Pages:

872-875

DOI:

https://doi.org/10.4028/www.scientific.net/AMM.727-728.872

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Online since:

January 2015

Authors:

Wen Bo Na*, Qing Feng Jiang, Zhi Wei Su

Keywords:

Fault Diagnosis, Improved Extreme Learning Machine, Pumping Well, Wavelet Transform (WT)

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* - Corresponding Author

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