Bayesian Wavelet Methodology for Damage Detection of Thermal Protection System Panels
This paper presents an intelligent computational methodology for loose bolt detection in thermal protection panels, considering uncertainties in sensed data. The proposed methodology is based on the integration of a dynamic artificial neural network, wavelet signal analysis, and Bayesian probabilistic assessment. A dynamic fuzzy wavelet neural network model is employed to perform the multiple input/multiple output nonparametric system identification of the panel using time series data obtained from the panel under a healthy condition. The trained model is used to predict dynamic responses of the structural system under unknown conditions. Both predicted and sensed time history data are decomposed into multiple time frequency resolutions using a discrete wavelet packet transform method. The wavelet packet component energy is computed in terms of the decomposed coefficients and used as a signal feature to detect loose bolts. The effectiveness of the selected features is assessed using both cross correlation and cross coherence metrics. The multivariate comparison in damage detection is handled by an interval based Bayesian hypothesis testing approach. The methodology is implemented to detect one loose bolt of a prototype thermal protection system panel with four mechanically bolted joints using experimental data collected at the U.S. Air Force Research Laboratory from seven different sensor configurations.