Power electronic circuits fault diagnosis method based on neural network ensemble

2015 ◽  
pp. 219-224
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Power Electronic ◽  
Neural Network Ensemble ◽  
Electronic Circuits ◽  
Power Electronic Circuits ◽  
Diagnosis Method

Fault Diagnosis Method Based on KPCA and Selective Neural Network Ensemble

2014 ◽  
Vol 915-916 ◽  
pp. 1272-1276 ◽  
Author(s):  
Yan Yan Pang ◽  
Hai Ping Zhu ◽  
Fan Mao Liu
Keyword(s):  
Neural Network ◽  
Fault Diagnosis ◽  
Binary Particle Swarm Optimization ◽  
Large Network ◽  
Neural Network Ensemble ◽  
Tennessee Eastman Process ◽  
Training Time ◽  
Diagnosis Method ◽  
Neural Network Algorithm ◽  
Bagging Method

Aiming at the problems of less study sample, large network scale and long training time existing in current fault diagnosis field, we develop a new method based on KPCA and selective neural network ensemble. First, reducing the data size by using KPCA to extract the sample features. Then achieving a selective neural network ensemble method based on improved binary particle swarm optimization algorithm (IBPSOSEN), and combining the two methods for fault diagnosis. In selective neural network algorithm, bagging method is used to take a number of different training sets of fault samples to solve the problem of less fault samples. Finally, simulation experiments and comparisons over Tennessee Eastman Process (TE) demonstrate the effectiveness and feasibility of the proposed method.

scholarly journals Fault Diagnosis Method of Power Electronic Converter Based on Broad Learning

Complexity ◽  
2020 ◽  
Vol 2020 ◽  
pp. 1-9
Author(s):  
Ran Han ◽  
Rongjie Wang ◽  
Guangmiao Zeng
Keyword(s):  
Fault Diagnosis ◽  
Learning System ◽  
Power Electronic ◽  
Fault Type ◽  
Fault Features ◽  
Three Phase ◽  
Power Electronic Circuits ◽  
Bridge Rectifier ◽  
Diagnosis Method ◽  
Sparse Autoencoder

In order to realize the unsupervised extraction and identification of fault features in power electronic circuits, we proposed a fault diagnosis method based on sparse autoencoder (SAE) and broad learning system (BLS). Firstly, the feature is extracted by the sparse autoencoder, and the fault samples and feature vectors are combined as the input of the broad learning system. The broad learning system is trained based on the error precision step update method, and the system is used to the fault type identification. The simulation results of the thyristor fault diagnosis of the three-phase bridge rectifier circuit show that the method is effective and has better performance than other traditional methods.

Sign in / Sign up

Export Citation Format

Share Document