Intelligent Bolt Loosening Detection in Transmission Towers Using Acoustic Signature Analysis and Machine Learning

The structural stability of transmission towers critically depends on the integrity of bolted connections, necessitating accurate bolt loosening detection for power grid safety. Traditional methods, such as manual inspection and hammering-based auditory analysis, suffer from inefficiency and inaccuracy due to environmental noise and subjective judgment. This paper proposes a novel machine learning framework for intelligent bolt loosening detection using acoustic signature analysis. The framework integrates multi-channel acoustic data from strategically placed sensors, extracting Mel-Frequency Cepstral Coefficients (MFCCs) through pre-emphasis, framing–windowing, the Fourier transform, Mel-filter bank processing, and the discrete cosine transform. Adversarial training is employed to suppress noise interference and hammering force variability by augmenting training data with perturbed samples. Experimental validation on 110 kV and 220 kV transmission towers demonstrates the framework’s efficacy: the Support Vector Machine (SVM) achieves 89.93% accuracy, 86.26% precision, 84.89% recall, and 84.91% F1 score, outperforming Decision Tree (86.7% accuracy), K-Nearest Neighbors (89.0%), Random Forest (84.86%), and XGBoost (89.47%). The proposed solution enables reliable, scalable bolt loosening detection, significantly advancing intelligent maintenance for power transmission infrastructure.