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Abstract
As the number of space debris (also called meteoroid/orbital debris-M/OD) increases in recent years, the hypervelocity-impact (HVI) events of M/OD on spacecrafts have become one of the most main risks threatening human activity in space. For the automatical M/OD risk assessment, some effective nondestructive testing (NDT) methods are critical to realizing the evaluation of the HVI damages. In this paper, a novel HVI damage evaluation method based on the active infrared thermal wave image detection technology with multi-objective feature extraction optimization (MO-FEO) is proposed to achieve the quantitative evaluation of M/OD HVI damages. For the precise selection of representative temperature point in thermal infrared image data, the proposed MO-FEO method has the advantage not only of considering the difference among temperature points in different thermal temperature categories but also considering the correlation among temperature points of each thermal temperature category. The multi-objective feature extraction problem decomposed by Tchebycheff aggregation is used to seek the representative temperature points through an evolution process brought the selection pressure and fitness value. In addition to the MO-FEO frame, the variable step search and classification of temperature points are also implemented in the HVI damage evaluation strategy to improve efficiency. Some experimental results of infrared detection for the real M/OD HVI test articles are proposed to illustrate the effectiveness of the proposed method.
Highlights
With the increasing human activities in space, more and more spacecrafts have been sent into the earth’s orbit or deep space [1], [2]
The automatical M/OD risk assessment is critical for various spacecraft, and the HVI damage evaluation based on some effective nondestructive testing (NDT) technologies is an important component of it
The solutions of the multi-objective feature extraction problems are used to generate Pareto front (PF) and select the representative temperature point, and thereby the quantitative damage evaluation and efficient risk assessment are realized in M/OD HVI event
Summary
With the increasing human activities in space, more and more spacecrafts have been sent into the earth’s orbit or deep space [1], [2]. The solutions of the multi-objective feature extraction problems are used to generate Pareto front (PF) and select the representative temperature point, and thereby the quantitative damage evaluation and efficient risk assessment are realized in M/OD HVI event. A. MULTI-OBJECTIVE FEATURE EXTRACTION OPTIMIZATION ALGORITHM FRAMEWORK In this paper, we investigate mainly the seeking problem of the representative temperature point for multi-performance consideration of difference and correlation. Step a8) Select the best compromise solution from i NDS, i ∈ (1, 2, ..., L), that is, determine representative temperature points from L categories: 1) Compute the membership i μi i X k of the i-th objective function value of the k-th solution in the i NDS according to the formula:. The MO-FEO algorithm can avoid the problem existed in [19], [20], that is, the selected representative temperature point may be weakly correlated with the transient thermal response of other similar temperature points under the premise of ensuring the difference
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