کد مقاله | کد نشریه | سال انتشار | مقاله انگلیسی | نسخه تمام متن |
---|---|---|---|---|
5472112 | 1519914 | 2017 | 42 صفحه PDF | دانلود رایگان |
عنوان انگلیسی مقاله ISI
Optimal sensor placement for deployable antenna module health monitoring in SSPS using genetic algorithm
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کلمات کلیدی
موضوعات مرتبط
مهندسی و علوم پایه
سایر رشته های مهندسی
مهندسی هوافضا
پیش نمایش صفحه اول مقاله
چکیده انگلیسی
The concept of space solar power satellite (SSPS) is an advanced system for collecting solar energy in space and transmitting it wirelessly to earth. However, due to the long service life, in-orbit damage may occur in the structural system of SSPS. Therefore, sensor placement layouts for structural health monitoring should be firstly considered in this concept. In this paper, based on genetic algorithm, an optimal sensor placement method for deployable antenna module health monitoring in SSPS is proposed. According to the characteristics of the deployable antenna module, the designs of sensor placement are listed. Furthermore, based on effective independence method and effective interval index, a combined fitness function is defined to maximize linear independence in targeted modes while simultaneously avoiding redundant information at nearby positions. In addition, by considering the reliability of sensors located at deployable mechanisms, another fitness function is constituted. Moreover, the solution process of optimal sensor placement by using genetic algorithm is clearly demonstrated. At last, a numerical example about the sensor placement layout in a deployable antenna module of SSPS is presented, which by synthetically considering all the above mentioned performances. All results can illustrate the effectiveness and feasibility of the proposed sensor placement method in SSPS.
ناشر
Database: Elsevier - ScienceDirect (ساینس دایرکت)
Journal: Acta Astronautica - Volume 140, November 2017, Pages 213-224
Journal: Acta Astronautica - Volume 140, November 2017, Pages 213-224
نویسندگان
Chen Yang, Xuepan Zhang, Xiaoqi Huang, ZhengAi Cheng, Xinghua Zhang, Xinbin Hou,