Advanced condition-based self-monitoring of composites damaged area under multiple impacts using Monte Carlo based prognostics

Abstract

Studies on self-sensing system under multiple impacts are limited. Furthermore, real-time prognostics research using electromechanical behavior for impact-damage growth is rare and the impact-damaged area analysis has limited in self-sensing. In this paper, the health state of the carbon-fiber-reinforced plastic samples were monitored in real time utilizing self-sensing data. Damage analysis was conducted through C-scan and crosssectional analysis, and the results were compared and correlated with those of failure analysis based on realtime electromechanical behavior during multiple impacts. Moreover, the relationship between electromechanical behavior and the impact-damaged area was investigated. The damage propagation during multiple impacts was identified in real time. Furthermore, the electromechanical behavior was predicted to prognosticate the damage propagation in the samples under multiple impacts using a particle filter. The RMSE of the impactdamaged area determined from the predicted electromechanical behavior using real-time prognostics tools was lower than 15 mm2. Moreover, the prediction accuracy according to data acquired was investigated. An advanced condition-based monitoring methodology can monitor current and future health states and damage propagation under 2 J and 3 J of multiple impacts that overcomes the previous self-sensing research. Therefore, this study showed high applicability and guidelines for future self-sensing research fields.