EMI-Based Damage Identification for Beam Structures

2014 ◽  
Vol 1081 ◽  
pp. 358-362 ◽  
Author(s):  
Yu Xiang Zhang ◽  
Jian Hai Yang ◽  
Fu Hou Xu ◽  
Jia Zhao Chen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Damage Identification ◽  
Particle Swarm ◽  
Global Search ◽  
Search Technique ◽  
Swarm Optimization ◽  
Identification Method ◽  
Basic Particle

A damage identification method is proposed to identify the damage style and the damage parameters. By driving a pair of PZT patches out phase and in phase, the electric admittance of the PZT is obtained. The damage parameters are then identified from the changes of the admittance spectra caused by the appearance of damage. By comparing the identification result, the damage style can be determined and the damage parameters can be obtained. The middle basic particle swarm optimization algorithm is employed as a global search technique to back-calculate the damage. Experiments are carried out on beams. The results demonstrate that the proposed method is able to identify the damage style, and can effectively and reliably locate and quantify the damage in the beam.

Edge reconstructions of black phosphorene: a global search

Nanoscale ◽  
2021 ◽  
Vol 13 (7) ◽  
pp. 4085-4091
Author(s):  
Yue Liu ◽  
Da Li ◽  
Tian Cui
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Global Search ◽  
Swarm Optimization ◽  
Black Phosphorene

A global search of black phosphorene edge structures are performed based on the particle swarm optimization algorithm.

scholarly journals A Local and Global Search Combined Particle Swarm Optimization Algorithm and Its Convergence Analysis

2014 ◽  
Vol 2014 ◽  
pp. 1-11 ◽  
Author(s):  
Weitian Lin ◽  
Zhigang Lian ◽  
Xingsheng Gu ◽  
Bin Jiao
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Global Search ◽  
Fine Tuning ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Large Size ◽  
Swarm Algorithm

Particle swarm optimization algorithm (PSOA) is an advantage optimization tool. However, it has a tendency to get stuck in a near optimal solution especially for middle and large size problems and it is difficult to improve solution accuracy by fine-tuning parameters. According to the insufficiency, this paper researches the local and global search combine particle swarm algorithm (LGSCPSOA), and its convergence and obtains its convergence qualification. At the same time, it is tested with a set of 8 benchmark continuous functions and compared their optimization results with original particle swarm algorithm (OPSOA). Experimental results indicate that the LGSCPSOA improves the search performance especially on the middle and large size benchmark functions significantly.

Research on Optimization and Verification Method of Sensor Arrangement in the Chemical and Volume Control System

2021 ◽  
Author(s):  
Gui Zhou ◽  
Hang Wang ◽  
Minjun Peng
Keyword(s):  
Particle Swarm Optimization ◽  
Control System ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Volume Control ◽  
Particle Swarm Algorithm ◽  
Swarm Optimization ◽  
Basic Particle ◽  
Swarm Algorithm

Abstract In order to avoid the nuclear accidents during the operation of nuclear power plants, it is necessary to always monitor the status of relevant facilities and equipment. The premise of condition monitoring is that the sensor can provide sufficient and accurate operating parameters. Therefore, the sensor arrangement must be rationalized. As one of the nuclear auxiliary systems, the chemical and volume control system plays an important role in ensuring the safe operation of nuclear power plants. There are plenty of sensor measuring points arranged in the chemical and volume control system. These sensors are not only for detecting faults, but also for running and controlling services. Particle swarm algorithm has many applications in solving the problem of sensor layout optimization but the disadvantage of the basic particle swarm optimization algorithm is that the parameters are fixed, the particles are single, and it is easy to fall into the local optimization. In this paper, the basic particle swarm optimization algorithm is improved by Non-linearly adjusting inertia weight factor, asynchronously changing learning factor, and variating particle. The improved particle swarm optimization algorithm is used to optimize the sensor placement. The numerical analysis verified that a smaller number of sensors can meet the fault detection requirements of the chemical and volume control system in this paper, and Experiments have proved that the improved particle swarm algorithm can improve the basic particle swarm algorithm, which is easy to fall into the shortcomings of local optimization and single particles. This method has good applicability, and could be also used to optimize other systems with sufficient parameters and consistent objective function.

scholarly journals UCPSO: A Uniform Initialized Particle Swarm Optimization Algorithm with Cosine Inertia Weight

2021 ◽  
Vol 2021 ◽  
pp. 1-18
Author(s):  
Jian Zhang ◽  
Jianan Sheng ◽  
Jiawei Lu ◽  
Ling Shen
Keyword(s):  
Particle Swarm Optimization ◽  
Optimization Algorithm ◽  
Particle Swarm Optimization Algorithm ◽  
Particle Swarm ◽  
Global Search ◽  
Local Optimum ◽  
Exploration And Exploitation ◽  
Swarm Optimization ◽  
Inertia Weight ◽  
And Performance

The particle swarm optimization algorithm (PSO) is a meta-heuristic algorithm with swarm intelligence. It has the advantages of easy implementation, high convergence accuracy, and fast convergence speed. However, PSO suffers from falling into a local optimum or premature convergence, and a better performance of PSO is desired. Some methods adopt improvements in PSO parameters, particle initialization, or topological structure to enhance the global search ability and performance of PSO. These methods contribute to solving the problems above. Inspired by them, this paper proposes a variant of PSO with competitive performance called UCPSO. UCPSO combines three effective improvements: a cosine inertia weight, uniform initialization, and a rank-based strategy. The cosine inertia weight is an inertia weight in the form of a variable-period cosine function. It adopts a multistage strategy to balance exploration and exploitation. Uniform initialization can prevent the aggregation of initial particles. It distributes initial particles uniformly to avoid being trapped in a local optimum. A rank-based strategy is employed to adjust an individual particle’s inertia weight. It enhances the swarm’s capabilities of exploration and exploitation at the same time. Comparative experiments are conducted to validate the effectiveness of the three improvements. Experiments show that the UCPSO improvements can effectively improve global search ability and performance.

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