Lorentz attractor excitation-based bolt loosening identification using enhanced transformer

Lorentz attractor excitation-based damage identification is a strategy with the advantage of capturing the structure’s nonlinear dynamic effects. In this study, bolt loosening in a frame structure was focused using Lorentz attractor-based chaotic signals as excitations. Nonlinear responses were recorded and damages of bolt looseness at different locations were considered. The structural damages could be revealed in the state-space plot of the responses. A state space curvature reconstruction method was introduced to enhance the key features of the nonlinear responses. A small-sample damage identification is performed using a Transformer with an accuracy of 92.38%. The advantages of the proposed method over conventional deep learning algorithms were validated.