Hypervelocity impact damage pattern recognition on aluminum plates based on Bayesian Regularization BP neural network

Damage patter recognition is a significant function module of on-board monitoring technology for space debris hypervelocity impact on spacecrafts. Based on hypervelocity impact acoustic emission on aluminum plates, hypervelocity impact acoustic emission signals were obtained through experiments. Combined with the accurate source location method for virtual wave front, specific time-frequency analysis and wavelet decomposition, the research extracted and optimized the relevant parameters of the damage pattern from the hypervelocity impact acoustic emission signals, thereby developing a Bayesian Regularization BP neural network for damage-pattern recognition and successfully recognizing the pit and hole damage patterns in an aluminum plate.