Research on percussion-based bolt looseness monitoring under noise interference and insufficient samples

Abstract

Loose bolts are the key cause of tower collapse. The maintenance of bolts in transmission towers remains at the stage of individual inspection, and it is urgent to develop practical bolt looseness monitoring methods for engineering applications. The percussion-based method has shown its full potential due to not relying on fixed signal generators and sensors. However, its performance can severely degrade under noise interference and data scarcity. To address this problem, the new voiceprint feature that resist noise interference and classifier that alleviate sample shortages are proposed in this study, namely all-pole group delay function (APGDF) and prototypical networks. The proposed new bolt torque monitoring method is composed of the two. Under the condition that each bolt torque has only 50 percussion sound samples and −6dB signal-to-noise ratio (SNR), the accuracy of the proposed method for single bolt looseness monitoring is 77.87 %, and the accuracy of multi-bolt looseness monitoring is 80.60 %, which is significantly better than existing feature-based methods. In addition, the monitoring of bolt looseness with highly few samples and the data dimensionality reduction analysis are carried out to demonstrate the superiority of this method. The percussion point needs to be close to the loose bolt to achieve the best monitoring effect, and the percussion intensity does not affect the monitoring effect. The proposed method is expected to become the fundamental mode of bolt looseness monitoring in engineering practice.