Bayesian Wavelet Methodology for Damage Detection of Thermal Protection System Panels

Abstract

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.