Hydraulic System Fault Diagnosis Based on EMD and Improved PSO-Elman ANN

Abstract:

The element parameters of engineering machinery hydraulic system are detected, the fault eigenvector is extracted, and the information is applied to neural network fault diagnosis. Experience mode decomposition (EMD) is used to extract fault characteristic vectors in this paper, combined with the pressure, temperature and flow rate of dominant signal as neural network's inputs. In addition, the paper improves the Elman neural network learning algorithm by PSO algorithm. It can effectively increase network convergence rate and computing power. The particle swarm is used to optimize Elman neural network weights and the threshold value and then applied in the fault diagnosis system by training the network. The results show that the method increases the neural network convergence rate and reduces diagnoses error.

• Periodical: Applied Mechanics and Materials (Volumes 284-287)
• Pages: 2936-2940
• DOI: https://doi.org/10.4028/www.scientific.net/AMM.284-287.2936
• Online since: January 2013
• Authors: Zeng Shou Dong, Xiao Yu Zhang, Jian Chao Zeng
• Keywords: Elman Neural Network (ENN), Experience Mode Decomposition, Fault Diagnosis, PSO Algorithm

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