基于混沌超声波激励的螺栓连接松动检测研究

Under severe loading conditions, bolted joints in assembled structures may loosen or fail. Reliability detection for early looseness in connection positions is significant to ensure the reliability and safety of structures. Here, high-frequency ultrasonic signals with a chaotic feature were adopted to excite a connection structure to be detected to acquire dynamic response signals of the structure, and use the nonlinear time series analysis method to extract feature parameters to characterize bolt looseness. Taking a typical bolted lap beam as the study object and piezoelectric ceramic pieces as units to collect excitation and response signals, the structure's response signals were demodulated and reconstructed in a phase space. Lyapunov dimension to characterize an attractor's whole features and ALAVR to reflect an attractor's local features were extracted and taken as looseness indexes. Test results showed that the proposed method can effectively detect the looseness state of bolted joints; compared with Lyapunov dimension, an attractor's feature parameter ALAVR can better detect the decline of pre-tightening force in bolts, ALAVR can effectively detect early bolt looseness.