Defect Sizing Simulation Studies for the Tone-Burst Eddy Current Thermography Using Genetic Algorithm Based Inversion

Purpose – Reducing eddy current losses in magnets of electrical machines can be obtained by means of several techniques. The magnet segmentation is the most popular one. It imposes the least restrictions on machine performances. This paper investigates the effectiveness of the magnet circumferential segmentation technique to reduce these undesirable losses. The full and partial magnet segmentation are both studied for a frequency range from few Hz to a dozen of kHz. To increase the efficiency of these techniques to reduce losses for any working frequency, an optimization strategy based on coupling of finite elements analysis and genetic algorithm is applied. The purpose of this paper is to define the parameters of the total and partial segmentation that can ensure the best reduction of eddy current losses. Design/methodology/approach – First, a model to analyze eddy current losses is presented. Second, the effectiveness of full and partial magnet circumferential segmentation to reduce eddy loss is studied for a range of frequencies from few Hz to a dozen of kHz. To achieve these purposes a 2-D finite element model is developed under MATLAB environment. In a third step of the work, an optimization process is applied to adjust the segmentation design parameters for best reduction of eddy current losses in case of surface mounted permanent magnets synchronous machine. Findings – In case of the skin effect operating, both full and partial magnet segmentations can lead to eddy current losses increases. Such deviations of magnet segmentation techniques can be avoided by an appropriate choice of their design parameters. Originality/value – Few works are dedicated to investigate partial magnet segmentation for eddy current losses reduction. This paper studied the effectiveness and behaviour of partial segmentation for different frequency ranges. To avoid eventual anomalies related to the skin effect an optimization process based on the association of the finite elements analysis to genetic algorithm method is adopted.

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Rapid Defect Reconstruction Based on Genetic Algorithm and Similar Model in Remote Field Eddy Current Non-Destructive Testing

Applied Mechanics and Materials ◽

10.4028/www.scientific.net/amm.518.269 ◽

2014 ◽

Vol 518 ◽

pp. 269-274 ◽

Cited By ~ 2

Author(s):

Xiao Jie Xu ◽

Wen Lin Pei

Keyword(s):

Genetic Algorithm ◽

Finite Element ◽

Eddy Current ◽

Non Destructive Testing ◽

Similar Model ◽

Destructive Testing ◽

Defect Reconstruction ◽

Element Computation ◽

Non Destructive ◽

Finite Element Computation

Defect reconstruction of remote field eddy current belonged to electromagnetic inverse problem in materia Non-Destructive Testing. In view of disadvantages in present defect reconstruction methods, such as over-long time or excessive need for training samples, a new defect reconstruction method based on similar model and genetic algorithm was investigated as follows: similar model between non-axisymetric defect and axisymetric defect was represented, and proportionality factor was deduced by skin depth equation. As a result, the time-consuming forward problem solution in defect reconstruction, which using 3D finite element computation conventionally, was simplified to 2D finite element computation. At the same time, a method designed to accelerate convergence of genetic algorithm was adopted, and more times saving were obtained. Computation results show that, total defect reconstruction time is reduced to 7.6%, compared with directly 3D finite elemet computation, while reconstruction accuracy remains almost the same.

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GA BASED SENSOR PLACEMENT AND CONTROL DESIGN FOR REACTIVE NAVIGATION OF MOBILE ROBOTS

International Journal of Information Acquisition ◽

10.1142/s0219878905000477 ◽

2005 ◽

Vol 02 (02) ◽

pp. 77-91 ◽

Cited By ~ 3

Author(s):

XIAOCHUAN WANG ◽

SIMON X. YANG ◽

MAX Q.-H. MENG

Keyword(s):

Genetic Algorithm ◽

Mobile Robots ◽

Control Structure ◽

Controller Design ◽

Sensor Placement ◽

Control Design ◽

Simulation Studies ◽

Optimal Sensor Placement ◽

Reactive Navigation ◽

And Control

In this paper, a novel genetic algorithm based approach is proposed for optimal sensor placement and controller design of a mobile robot to facilitate its reactive navigation and obstacle avoidance in unknown environments. The mobile robots considered in this paper have flexible sensor and control structure. A genetic algorithm is developed to evolve the parameters of optimal sensor placement and controller design simultaneously. The effectiveness of the proposed GA based co-evolution approach to robot sensor placement and control design is demonstrated by simulation studies.

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