Bayesian network for E/M impedance-based damage identification

A Bayesian network is a probabilistic representation of the multiple cause-effect dependency relationships in a domain. It incorporates human reasoning to deal with sparse data availability and to determine the probabilities of uncertain events. In this paper, a Bayesian network is adopted to model the problem of damage location identification. The damage identification method uses the natural frequency shifts and the undamaged mode shapes of the structure to identify the damage location. The frequency shifts are extracted numerically from a finite-element (FE) model and experimentally from the electromechanical (e/m) admittance signatures of the smart piezoelectric (PZT) transducer bonded to the structure. The undamaged mode shapes are determined from the FE model of the undamaged structure. To incorporate a suitable Bayesian network model, issues of variable selection, variable dependency, probabilistic inference, and error modeling are discussed. The performance of the implemented Bayesian network is verified using both numerical and experimental data. The model is able to accurately determine the damage location. with only a Subset of frequency shift data, and eliminated the model errors.