A novel neural-network approach of analog fault diagnosis based on kernel discriminant analysis and particle swarm optimization

2012 ◽  
Vol 12 (2) ◽  
pp. 904-920 ◽  
Author(s):  
Yingqun Xiao ◽  
Lianggui Feng
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Discriminant Analysis ◽  
Particle Swarm ◽  
Network Approach ◽  
Kernel Discriminant Analysis ◽  
Neural Network Approach ◽  
Swarm Optimization ◽  
Analog Fault Diagnosis

Study on Fault Diagnosis of Gear-Box

2013 ◽  
Vol 722 ◽  
pp. 270-275
Author(s):  
Xiao Hui Chen
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Differential Evolution ◽  
Optimization Algorithm ◽  
Bp Neural Network ◽  
Particle Swarm ◽  
Neural Network Approach ◽  
Swarm Optimization ◽  
Gear Box

A BP neural network approach based on differential evolution and particle swarm optimization is developed for fault diagnosis of gear-box. The approach takes a novel kind of optimization algorithm, i.e., differential evolution and particle swarm optimization algorithm, to train BP neural work. The feasibility and effectiveness of this new approach is validated and illustrated by the study cases of fault diagnosis on gear-box. The diagnosis results show that the BP neural network based on differential evolution and particle swarm optimization has a better training performance, better convergence behavior, as well as better diagnosis ability than BP neural network. Owing to the flexibility of this approach, it can be adapted to other problems very easily.

Quantitative Analysis for SF6 and its Compositions in GIS

2012 ◽  
Vol 562-564 ◽  
pp. 1336-1339
Author(s):  
Hai Lun Wang ◽  
Jian Wei Shen
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Quantitative Analysis ◽  
Fault Diagnosis ◽  
Traditional Method ◽  
Particle Swarm ◽  
Prediction Errors ◽  
Swarm Optimization ◽  
Training Analysis ◽  
Derivatives Of

In this paper, a method for GIS equipment fault diagnosis by the analysis of volume fractions of the derivatives of SF6 gas inside GIS equipment is presented. For the method, based on the differential spectra method, a neural network model and the particle swarm optimization are used for training analysis of infrared spectra, to realize the quantitative analysis of specific derivatives. The experimental results show that the prediction errors obtained by particle swarm optimization training are markedly superior to prediction errors obtained using the traditional method.

Power Transform Fault Diagnosis Based on the Adaptive Mutation Particle Swarm Optimization

2013 ◽  
Vol 756-759 ◽  
pp. 3804-3808
Author(s):  
Zhi Mei Duan ◽  
Jia Tang Cheng
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Recognition Rate ◽  
Particle Swarm ◽  
Adaptive Mutation ◽  
Bp Network ◽  
Swarm Optimization ◽  
Fault Recognition

In order to improve the accuracy of fault diagnosis of power transformer, in this paper, a method is proposed that optimize the weight of BP neural network by adaptive mutation particle swarm optimization (AMPSO). According to the characteristic of transformer fault, the optimized neural network is used to diagnose fault of the power transformer. Individual particles action is amended by this algorithm and local minima problems of the standard PSO and BP network are overcooked. The experimental results show that, the method can classify transformer faults, and effectively improve the fault recognition rate.

WNN Model Based on Particle Swarm Optimization for Fault Diagnosis in Analog Circuit

2013 ◽  
Vol 427-429 ◽  
pp. 1048-1051
Author(s):  
Xu Sheng Gan ◽  
Hao Lin Cui ◽  
Ya Rong Wu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Convergence Rate ◽  
Analog Circuit ◽  
Particle Swarm ◽  
Pso Algorithm ◽  
Wavelet Neural Network ◽  
Swarm Optimization ◽  
Fast Convergence Rate

In order to diagnose the fault in analog circuit correctly, a Wavelet Neural Network (WNN) method is proposed that uses the Particle Swarm Optimization (PSO) algorithm to optimize the network parameters. For the improvement of convergence rate in WNN based on PSO algorithm, a compressing method in research space is introduced into the traditional PSO algorithm to improve the convergence in WNN training. The simulation shows that the proposed method has a good diagnosis with fast convergence rate for the fault in analog circuit.

Application of BP Neural Network Based on Immune Particle Swarm Optimization for Fault Diagnosis of Power Transformer

2013 ◽  
Vol 448-453 ◽  
pp. 3605-3609
Author(s):  
Yu Xin Zhang ◽  
Yu Liu
Keyword(s):  
Neural Network ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Bp Neural Network ◽  
Power Transformer ◽  
Particle Swarm ◽  
Swarm Optimization ◽  
Network Training ◽  
Gradient Descent Algorithm ◽  
Ipso Algorithm

Cloing and hypermutation of immune theory were used in optimization on particle swarm optimization (PSO), an immune particle swarm optimization (IPSO) algorithm was proposed , which overcome the problem of premature convergence on PSO. IPSO was used in BP Neural Network training to overcome slow convergence speed and easily getting into local dinky value of gradient descent algorithm. BP Neural Network trained by IPSO was used to fault diagnosis of power transformer, it has high accuracy after experimental verification and to meet the power transformer diagnosis engineering requirements.

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