As a significant function module of space debris hypervelocity impact On-board monitoring technology, damage identification is the emphasis and difficulty of the research. Based on hypervelocity impact acoustic emission on aluminum plate, obtain varieties of hypervelocity impact acoustic emission signals by experiments. Combine with the accurate source location method with virtual wave front, specific time-frequency analysis and wavelet decomposition, extract and optimize the relevant parameters of damage pattern from hypervelocity impact acoustic emission signals, modeling a Bayesian Regularization BP neural network for damage pattern recognition method, carry out the pit and hole damage patterns recognition in aluminum plate.
Key words
space debris /
hypervelocity impact /
acoustic emission /
neural network /
damage pattern recognition
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