针对输电塔螺栓数量多、人工松动检测效率低的问题，该文提出了一种基于声音识别的输电塔螺栓松动检测方法。该方法构建梅尔频率倒谱系数（MFCC）声学特征与螺栓预紧力状态的非线性映射模型，揭示螺栓松动程度与频域能量分布的内在关联；建立多指标综合评价体系，在2种样本不平衡比例(4:1和2:1)下，系统对比了随机森林(RF)、极端梯度提升树(XGBoost)、深度森林(DF)、支持向量机(SVM)及残差网络(ResNet)的检测性能，分析了传统机器学习与深度学习模型在少样本场景下的适用差异。最后通过现场试验验证该方法的可行性，并探究影响声学识别效果的关键因素。结果表明：在单颗螺栓松动情况下，SVM模型识别准确率最优（可达96.1%），其AUC值为0.918，趋近于1，证明该模型在样本不平衡条件下具有良好的鲁棒性；在多螺栓松动场景中，多螺栓耦合振动会强化声学特征，使MFCC蕴含更多差异化特征，整体识别准确率进一步提升至98.2%。螺栓安装位置与激励点位对检测精度无明显影响，而信号采集距离增加会导致识别精度显著降低。该研究可为输电塔螺栓预紧力状态的快速、自动化检测提供新型的技术思路。

To address the issues of the large number of bolts on transmission towers and the low efficiency of manual looseness detection, this paper proposes a sound-based method for detecting bolt looseness on transmission towers. The method establishes a nonlinear mapping model between Mel-frequency cepstral coefficients (MFCC) acoustic features and the preload state of bolts, revealing the intrinsic relationship between the degree of bolt looseness and the frequency-domain energy distribution; A multi-indicator comprehensive evaluation system was established. Under two sample imbalance ratios (4:1 and 2:1), the detection performance of Random Forest (RF), Extreme Gradient Boosting Tree (XGBoost), Deep Forest (DF), Support Vector Machine (SVM), and Residual Network (ResNet) was systematically compared, and the differences in applicability between traditional machine learning and deep learning models in low-sample-size scenarios were analyzed. Finally, the feasibility of this method was validated through field tests, and key factors influencing acoustic recognition performance were investigated. The results indicate that: in the case of a single loose bolt, the SVM model achieved the highest recognition accuracy (up to 96.1%), with an AUC of 0.918, approaching 1, demonstrating the model’s robustness under imbalanced sample conditions; in scenarios involving multiple loose bolts, the coupled vibrations of multiple bolts enhance acoustic features, enabling MFCC to capture more distinctive characteristics, and the overall recognition accuracy further improves to 98.2%. The installation location of the bolts and the excitation point have no significant impact on detection accuracy, whereas an increase in signal acquisition distance leads to a significant decrease in recognition accuracy. This study provides a novel technical approach for the rapid, automated detection of bolt preload conditions in transmission towers.