scholarly journals A Fault Detection Method Based on CPSO-Improved KICA

Entropy ◽  
2019 ◽  
Vol 21 (7) ◽  
pp. 668 ◽  
Author(s):  
Mingguang Liu ◽  
Xiangshun Li ◽  
Chuyue Lou ◽  
Jin Jiang
Keyword(s):  
Fault Detection ◽  
Fitness Function ◽  
Pso Algorithm ◽  
Independent Component ◽  
Local Optimum ◽  
Tennessee Eastman Process ◽  
Detection Rates ◽  
Kernel Independent Component Analysis ◽  
Optimal Kernel ◽  
Kernel Parameters

In view of the randomness in the selection of kernel parameters in the traditional kernel independent component analysis (KICA) algorithm, this paper proposes a CPSO-KICA algorithm based on Chaotic Particle Swarm Optimization (CPSO) and KICA. In CPSO-KICA, the maximum entropy of the extracted independent component is first adopted as the fitness function of the PSO algorithm to determine the optimal kernel parameters, then the chaotic algorithm (CO) is used to avoid the local optimum existing in the traditional PSO algorithm. Finally, this proposed algorithm is compared with Weighted KICA (WKICA) and PSO-KICA with Tennessee Eastman Process (TEP) as the benchmark. Simulation results show that the proposed algorithm can determine the optimal kernel parameters and perform better in terms of false alarm rates (FAR), detection latency (DL) and fault detection rates (FDR).

Missile Guidance Algorithm Design Using Particle Swarm Optimization

2013 ◽  
Vol 284-287 ◽  
pp. 2411-2415
Author(s):  
Chien Chun Kung ◽  
Kuei Yi Chen
Keyword(s):  
Objective Function ◽  
Fitness Function ◽  
Equations Of Motion ◽  
Algorithm Design ◽  
Solution Space ◽  
Pso Algorithm ◽  
Local Optimum ◽  
Missile Guidance ◽  
Pursuit Evasion ◽  
Guidance Algorithm

This paper presents a technique to design a PSO guidance algorithm for the nonlinear and dynamic pursuit-evasion optimization problem. In the PSO guidance algorithm, the particle positions of the swarm are initialized randomly within the guidance command solution space. With the particle positions to be guidance commands, we predict and record missiles’ behavior by solving point-mass equations of motion during a defined short-range period. Taking relative distance to be the objective function, the fitness function is then evaluated according to the objective function. As the PSO algorithm proceeds, these guidance commands will migrate to a local optimum until the global optimum is reached. This paper implements the PSO guidance algorithm in two pursuit-evasion scenarios and the simulation results show that the proposed design technique is able to generate a missile guidance law which has satisfied performance in execution time, terminal miss distance, time of interception and robust pursuit capability.

Fault Diagnosis of Rolling Bearing Based on Kernel Independent Component Analysis by Using Mixed Kernel Function

2013 ◽  
Vol 312 ◽  
pp. 593-596 ◽  
Author(s):  
Bo Zeng ◽  
An Hua Chen ◽  
Ling Li Jiang
Keyword(s):  
Fault Diagnosis ◽  
Kernel Function ◽  
Kernel Method ◽  
Rolling Bearing ◽  
Independent Component ◽  
Fusion Method ◽  
Bearing Fault Diagnosis ◽  
Weighted Fusion ◽  
Kernel Independent Component Analysis ◽  
Kernel Parameters

Studies have shown that the type of kernel function and parameters have a very important impact on the performance of the kernel method. Aiming at the requirement of rolling bearing fault diagnosis, this paper presents a mixed kernel function of kernel independent component and studies on the optimization of its kernel parameters. The mixed kernel function is constructed based on the weighted fusion method, and the kernel parameters are optimized by using the genetic algorithm. The improved kernel independent component method is used for fault diagnosis of rolling bearing, and the testing results demonstrate that it is an effective method.

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