A few-shot electromechanical impedance monitoring method based on a modified prototype network

Abstract

Bolt loosening monitoring is of great significance to warrant the reliability and safety of bolted structures. The electromechanical impedance (EMI)-based evaluation is effective to perceive bolt loosening. However, EMI signals are highly prone to contamination by temperature fluctuation. Deep learning (DL) based EMI is a promising technique for accurate damage detection in the temperature variation environment. However, DL needs a lot of data to train, which is usually very difficult to collect sufficient structural damage data in real word scenarios. This paper proposed a few-shot EMI monitoring method based on a modified prototype network for bolt looseness detection under temperature varying environment. The approach features a conversion method of the impedance signal to image based on the Hank matrix. A modified prototype network is then developed. Experimental study was carried out on a bolted joint. EMI signals under different bolt loosening conditions were measured in a temperature variation environment. An impedance analyzer and a self-made small lightweight monitoring device were both used to measure the EMI signals to test the cross domain scenario. The proposed method was compared with the transfer learning methods and other typical FSL methods. The experiment results show that the proposed few-shot EMI method can obviously improve the monitoring accuracy of bolt loosening with few samples.

Research on the influence of an adhesive layer on the monitoring signal of piezoelectric sensors

“…Ferreira et al [14] applied EMI to the metal cutting industry and monitored the correlation between roughness and EMI signals, verifying that this method is expected to replace traditional monitoring systems. Du et al [15] proposed a few-shot EMI monitoring method based on an improved prototype network for bolt looseness detection in temperature-changing environments. The experimental results showed that the proposed few-shot EMI method can significantly improve the monitoring accuracy of bolt looseness.…”

Damage detection and localization based on different types of actuators using the electromechanical impedance method in 3D-printed material

“…The EMI measurements of the various frequency bands were employed in a chessboard distance metric to determine the degree of damage under various temperature conditions [19]. Further, Du et al converted the EMI signal into an image based on the Hank matrix to monitor the bolt loosening in the temperature-varying environment [20]. The study in [21] presents an analysis that contrasts structural mechanical impedance and electromechanical admittance.…”