Research on Electronic Equipment Fault Diagnosis Based on Improved BP Algorithm

2010 ◽  
Author(s):  
Dong-sheng Xu
Keyword(s):  
Fault Diagnosis ◽  
Bp Algorithm ◽  
Electronic Equipment ◽  
Improved Bp Algorithm

Applied Technology on Artificial Neural Network in Fault Diagnosis System

2013 ◽  
Vol 859 ◽  
pp. 448-452
Author(s):  
Qi Zhu ◽  
Jian Li
Keyword(s):  
Neural Network ◽  
Artificial Neural Network ◽  
Fault Diagnosis ◽  
Membership Function ◽  
Back Propagation ◽  
Bp Algorithm ◽  
Bp Networks ◽  
Applied Technology ◽  
Fault Diagnosis System ◽  
Improved Bp Algorithm

This paper combined Rumelhart’s adding inertial impulse and dynamically adjusting the learning rate and proposed an improved algorithm to optimize the Back Propagation (BP) networks with applied technology. This improved BP networks is used to determining membership function and applied in fuzzy diagnosing vapor congealing equipment. The application results prove that the improved BP algorithm is effective and the convergence speed is accelerated and is much faster than the classic BP algorithm. The applied technology is very useful in the application course.

An Improved Neural Network Algorithm and its Application in Fault Diagnosis

2013 ◽  
Vol 765-767 ◽  
pp. 2355-2358
Author(s):  
Tai Shan Yan ◽  
Guan Qi Guo ◽  
Wu Li ◽  
Wei He
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Transformer ◽  
Convergence Speed ◽  
Bp Algorithm ◽  
Network Algorithms ◽  
Convergence Error ◽  
Neural Network Algorithm ◽  
Transformer Fault ◽  
Improved Bp Algorithm

Aiming at BP neural network algorithms limitation such as falling into local minimum easily and low convergence speed, an improved BP algorithm with two times adaptive adjust of training parameters (TA-BP algorithm) was proposed. Besides the adaptive adjust of training rate and momentum factor, this algorithm can gain appropriate permitted convergence error by adaptive adjust in the course of training. TA-BP algorithm was applied in fault diagnosis of power transformer. A fault diagnosis model for power transformer was founded based on neural network. The illustrational results show that this algorithm is better than traditional BP algorithm in both convergence speed and precision. We can realize a fast and accurate diagnosis for power transformer fault by this algorithm.

scholarly journals Classification of ETM+ Remote Sensing Image Based on Hybrid Algorithm of Genetic Algorithm and Back Propagation Neural Network

2013 ◽  
Vol 2013 ◽  
pp. 1-8 ◽  
Author(s):  
Haisheng Song ◽  
Ruisong Xu ◽  
Yueliang Ma ◽  
Gaofei Li
Keyword(s):  
Neural Network ◽  
Remote Sensing ◽  
Genetic Algorithm ◽  
Hybrid Algorithm ◽  
Local Minimum ◽  
Back Propagation ◽  
Remote Sensing Image ◽  
Back Propagation Neural Network ◽  
Bp Algorithm ◽  
Improved Bp Algorithm

The back propagation neural network (BPNN) algorithm can be used as a supervised classification in the processing of remote sensing image classification. But its defects are obvious: falling into the local minimum value easily, slow convergence speed, and being difficult to determine intermediate hidden layer nodes. Genetic algorithm (GA) has the advantages of global optimization and being not easy to fall into local minimum value, but it has the disadvantage of poor local searching capability. This paper uses GA to generate the initial structure of BPNN. Then, the stable, efficient, and fast BP classification network is gotten through making fine adjustments on the improved BP algorithm. Finally, we use the hybrid algorithm to execute classification on remote sensing image and compare it with the improved BP algorithm and traditional maximum likelihood classification (MLC) algorithm. Results of experiments show that the hybrid algorithm outperforms improved BP algorithm and MLC algorithm.

A Modified PSO-BP Algorithm in Hydraulic System Fault Diagnosis Application

2012 ◽  
Vol 182-183 ◽  
pp. 1145-1148 ◽  
Author(s):  
Zeng Shou Dong ◽  
Xiao Yu Zhang ◽  
Jian Chao Zeng
Keyword(s):  
Fault Diagnosis ◽  
Convergence Rate ◽  
Hydraulic System ◽  
Particle Swarm ◽  
Bp Algorithm ◽  
Particle Swarm Algorithm ◽  
Bp Network ◽  
Learning Factor ◽  
Swarm Algorithm ◽  
Modified Pso

BP neural network for failure pattern recognition has been used in hydraulic system fault diagnosis.However, its convergence rate is relatively small and always trapped at the local minima. So a new modified PSO-BP hydraulic system fault diagnosis method was proposed,which combined the respective advantages of particle swarm algorithm and BP algorithm. Firstly, the inertia weight and learning factor of the standard particle swarm algorithm was improved, then BP neural network’s weights and thresholds were optimized by modified PSO algorithm. BP network performance was ameliorated. The simulation results showed that this method improved the convergence rate of the BP network, and it could reduce the diagnostic errors.

Research on Fault Diagnosis Based on TEAMS

2012 ◽  
Vol 605-607 ◽  
pp. 797-801
Author(s):  
Yan Hong Liang ◽  
Qing Yi Wu ◽  
Dan Jin
Keyword(s):  
Fault Diagnosis ◽  
Performance Analysis ◽  
Design Process ◽  
Electronic Equipment ◽  
Diagnostic Strategy ◽  
Basic Design ◽  
Signal Modeling

The paper focuses on introducing the basic condition of the TEAMS and the basic design process of multi-signal modeling and diagnostic strategy. Moreover, it introduces the applications of TEAMS in fault diagnosis from three aspects: model establishment, performance analysis and diagnostic strategy. The superiority and effectiveness of above-mentioned method is verified by an instance and the results are useful to the fault diagnosis of complex electronic equipment.

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