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

2013 ◽  
Vol 284-287 ◽  
pp. 2936-2940
Author(s):  
Zeng Shou Dong ◽  
Xiao Yu Zhang ◽  
Jian Chao Zeng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convergence Rate ◽  
Hydraulic System ◽  
Learning Algorithm ◽  
Threshold Value ◽  
Pso Algorithm ◽  
Elman Neural Network ◽  
Neural Network Learning ◽  
Network Convergence

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.

scholarly journals Optimization of Machining Parameters in Milling Process of Inconel 718 under Surface Roughness Constraints

2021 ◽  
Vol 11 (5) ◽  
pp. 2137 ◽  
Author(s):  
Tian-Yau Wu ◽  
Chi-Chen Lin
Keyword(s):  
Neural Network ◽  
Surface Roughness ◽  
Artificial Neural Network ◽  
Inconel 718 ◽  
Cutting Parameters ◽  
Pso Algorithm ◽  
Milling Process ◽  
Machining Parameters ◽  
Elman Neural Network ◽  
Swarm Optimization

The objective of this research is to investigate the feasibility of utilizing the Elman neural network to predict the surface roughness in the milling process of Inconel 718 and then optimizing the cutting parameters through the particle swarm optimization (PSO) algorithm according to the different surface roughness requirements. The prediction of surface roughness includes the feature extraction of vibration measurements as well as the current signals, the feature selection using correlation analysis and the prediction of surface roughness through the Elman artificial neural network. Based on the prediction model of surface roughness, the cutting parameters were optimized in order to obtain the maximal feed rate according to different surface roughness constraints. The experiment results show that the surface roughness of Inconel 718 can be accurately predicted in the milling process and thereafter the optimal cutting parameter combination can be determined to accelerate the milling process.

An adaptive Elman neural network with C-PSO learning algorithm based pitch angle controller for DFIG based WECS

2016 ◽  
Vol 23 (5) ◽  
pp. 716-730 ◽  
Author(s):  
R Sitharthan ◽  
M Geethanjali
Keyword(s):  
Neural Network ◽  
Output Power ◽  
Pitch Angle ◽  
Learning Algorithm ◽  
Back Propagation ◽  
Pso Algorithm ◽  
Power Fluctuation ◽  
Elman Neural Network ◽  
Learning Capability ◽  
Wind Energy Conversion

Frequent variation in the wind flow affects the Wind Turbine (WT) to generate fluctuating output power and this can negatively impact the entire power network. This paper aims at modelling an Enhanced-Elman Neural Network (EENN) based pitch angle controller to mitigate the output power fluctuation in a grid connected Wind Energy Conversion System. The outstanding aspect of the proposed controller is that, they can smoothen the output power fluctuation, when the wind speed is above or below rated speed of the WT. The proposed EENN pitch controller is trained online using Gradient Descent (GD) algorithm and the network learning is carried out using Customized-Particle swarm optimization (C-PSO) algorithm. The C-PSO is adopted, in order to increase the learning capability of the training process by adjusting the networks learning rate. Further, the node connecting weights of the EENN is updated by means of GD algorithm using back-propagation methodology. The performance of the proposed controller is analysed using the simulation studies carried out in MATLAB /Simulink environment.

Research on Stall Aerodynamics Modeling Based on WNN Trained by AFS-PSO Hybrid Algorithm from Flight Data

2014 ◽  
Vol 602-605 ◽  
pp. 3173-3176 ◽  
Author(s):  
Xu Sheng Gan ◽  
Xue Qin Tang ◽  
Hai Long Gao
Keyword(s):  
Neural Network ◽  
Hybrid Algorithm ◽  
Pso Algorithm ◽  
Good Prediction ◽  
Swarm Optimization ◽  
Flight Data ◽  
Numerical Examples ◽  
Aerodynamic Modeling ◽  
Aerodynamic Model ◽  
Artificial Fish Swarm

To accurately depict the dynamic characteristics for aircraft stall by aerodynamic model, a Wavelet Neural Network (WNN) stall aerodynamic modeling method based on Particle Swarm Optimization (PSO) algorithm and Artificial Fish Swarm (AFS) algorithm is proposed. Numerical examples show that the proposed method has a good prediction precision, and it is also effective and feasible to build the aerodynamic model from flight data for aircraft stall.

HYDRAULIC SYSTEM FAULT DIAGNOSIS BASED ON EMD AND IMPROVED PSO-ELMAN ANN

2013 ◽  
Vol 37 (4) ◽  
pp. 1189-1197 ◽  
Author(s):  
Zengshou Dong ◽  
Xiaoyu Zhang ◽  
Jianchao Zeng
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Convergence Rate ◽  
Hydraulic System ◽  
Learning Algorithm ◽  
Threshold Value ◽  
Pso Algorithm ◽  
Elman Neural Network ◽  
Neural Network Learning ◽  
Network Convergence

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 the 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.

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