Percussion-based loosening detection method for multi-bolt structure using convolutional neural network DenseNet-CBAM

Abstract

Threaded fasteners are widely applied in mechanical systems, providing the functions of connection, fastening, and sealing. However, loosening is vulnerable to occurring in harsh environment. The importance of loosening detection cannot be emphasized. Percussion-based loosening detection method has attracted much attention due to the convenience and low cost. However, the simultaneous loosening detection of multiple-threaded fasteners based on percussion method is still a challenging issue that needs to be addressed. This study proposes a novel multi-bolt loosening detection method combining percussion method, and deep learning. The method consists of three integrated modules, that is, signal preprocessing, loosening information enhancement, and loosening detection modules. In the first module, variational mode decomposition is used to decompose the original signal into a series of intrinsic mode function to eliminate the interference of noise. In the second module, compressive sampling matching pursuit is applied to represent the denoised signal sparsely, and the sparse signal is fused with the denoised signal to enhance loosening information in the signal. Last, DenseNet-CBAM network structure combining attention mechanism is proposed for multiple classification task. Experimental results showed that the proposed method achieved the detection accuracy of more than 97% in three different types of mechanical structures with multiple-threaded fasteners, indicating its great potentials in engineering applications.