STRUCTURAL HEALTH MONITORING BASED ON INNER PRODUCT MATRIX AND DEEP LEARNING

Structural health monitoring methods using vibration responses only under ambient excitation are appealing as its convenience to realize on-line monitoring. The basic concepts and characteristics of structural characteristic parameter (i.e. inner product vector) based on the correlation analysis of time domain vibration response are reviewed. In order to extract more structural characteristic parameters from the existing test data, the inner product vector is extended to the inner product matrix by using several inner product vectors which are constructed by setting each measurement point as the reference measurement point. Furthermore, taking the inner product matrix as the structural characteristic parameter and combining the feature extraction ability of deep convolution neural network, a structural health monitoring method based on deep learning and inner product matrix is proposed. The experimental results of monitoring the bolt loosening of a typical aeronautical stiffened panel show that the bolt loosening position can be correctly located using the time domain vibration response only under ambient excitation.