Improved Particle Swarm Optimization Based Fault Diagnosis Approach for Power Electronic Devices

2009 ◽  
Author(s):  
Yan Yang ◽  
Ruqing Chen ◽  
Jinshou Yu ◽  
Ruqing Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Electronic Devices ◽  
Particle Swarm ◽  
Power Electronic ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Diagnosis Approach

Research on Knowledge Acquisition Based on Improved Particle Swarm Optimization

2013 ◽  
Vol 401-403 ◽  
pp. 1539-1542
Author(s):  
Xiu Hai Chen ◽  
Jun Bo Jia ◽  
Xu Yuan Li ◽  
Shi Ke Wang ◽  
Ye Zhao
Keyword(s):  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Knowledge Acquisition ◽  
Optimization Problem ◽  
Particle Swarm ◽  
Mutation Operator ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Density Evaluation ◽  
System Knowledge

In order to solve the problem of knowledge acquisition in equipment fault diagnosis,the paper introduces a method based on improved particle swarm optimization. Firstly the paper transforms the nonlinear equations which describe the system into optimization problem with constriction. Since the equation is nonlinear and multidimensional ,standard particle swarm cant solve the problem due to the weakness of premature. So one improved particle swarm optimization is proposed. During the evolution, density evaluation, clone and mutation operator is proposed under the thought of immunity. The results of simulation show that the immune particle swarm optimization can simulate effectively and acquire the system knowledge.

Research on Feature Selection Based on Improved Particle Swarm Optimization

2012 ◽  
Vol 591-593 ◽  
pp. 2651-2654
Author(s):  
Guo Qing Wang ◽  
Jun Bo Jia ◽  
Xu Yuan Li
Keyword(s):  
Feature Selection ◽  
Particle Swarm Optimization ◽  
Fault Diagnosis ◽  
Rocket Engine ◽  
Particle Swarm ◽  
Liquid Propellant ◽  
Swarm Optimization ◽  
Improved Particle Swarm Optimization ◽  
Liquid Propellant Rocket Engine ◽  
Propellant Rocket

Feature selection is one of key technologies for fault diagnosis. Especially for high dimensional data, Feature selection can not only find the feature subset with sufficient information, but also improve the classification accuracy and efficiency. In order to decrease the number of diagnosis parameter in fault diagnosis of Liquid-propellant Rocket Engine, the paper proposes one feature selection method based on improved particle swarm optimization, the method applies the quantum evolution thoughts to PSO. The particle is restricted in the range from -π/2 to 0, so the particle can correspond to the quantum angle. The parameter optimization function is designed. The improved algorithm can decrease the number of parameter in fault diagnosis of Liquid-propellant Rocket Engine from 25 to 6.

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