Bolt load looseness detection for slip-critical blind bolt based on wavelet analysis and deep learning

Abstract

Bolt preload is a crucial factor in high-strength bolt applications, particularly for the Slip-critical blind bolts (SCBBs). This study presents a novel nondestructive approach that leverages ultrasonic echo waves to accurately detect the state of bolt looseness, addressing a significant research gap in the field of blind bolt looseness detection. The proposed technique is uniquely suitable for blind bolted connections as it only necessitates access to a single side of the connection. Nine types of SCBBs were tested and obtained approximately 4000 ultrasonic echo signals with varying degrees of looseness. These signals have been transformed into image-based representations using wavelet analysis, and deep learning techniques were used to classify and predict the looseness level accurately. The performance of various damage assessment criteria, CNN models, and dataset sizes were evaluated and compared. The proposed method was validated by classifying 400 datasets with a validated accuracy of 97.30%.