Probability of the presence of damage estimated from an active sensor network in a composite panel of multiple stiffeners

Artificial damage in the form of a through-thickness hole in a composite panel of five stiffeners is identified using a guided wave-based damage diagnostic algorithm, which is based on the probability of the presence of damage in the monitoring area estimated using correlation coefficients of Lamb wave signals from an active sensor network. Without defining the detail features of individual wave components, this diagnostic algorithm focuses on the calibrated changes in wave signals due to the presence of damage. The probability of the presence of damage at each grid in the monitoring area is estimated. The area consisting of grids with probability values for the presence of damage above a threshold is subsequently identified for damage identification. The effect of networks of sensing paths and shapes of the affected zone of individual sensing paths on identifying the damage in the composite panel is investigated. The results demonstrate that the diagnostic algorithm, being computationally efficient and amenable to automated processing, can be used to identify damage in highly complex structures.