Fail-safe sensor distributions for impact detection in composite materials

This paper studies the problem of optimal sensor placement for impact detection and location in composite materials. The study involves a simple impact experiment on a composite box panel. The time-varying strain data are measured using piezoceramic sensors. An effective impact detection procedure is established using a neural network approach. The procedure determines the location and amplitude of impacts. A genetic algorithm is used to determine the optimum sensor positions for a diagnostic system. The main object of the paper is to study fail-safe distributions, i.e. those whose sub-distributions also have a low probability of detection error. The results are validated against an exhaustive search. The study shows that genetic algorithms combined with neural networks can be effectively used to find near-optimal sensor distributions for damage detection. The methods presented are generic and can be used in similar sensor position problems.