超声声学异常检测用于提前识别 CNC 钻头卡死
Acoustic Anomaly Detection for Early Identification of Drill Jamming
用超声波听声音就能提前发现钻头要卡死，Autoencoder 在 17 个钻头上一个故障都没漏，做工业预测维护的可以看看这份公开数据集。
arXiv 论文提出基于超声波传感器的声学异常检测系统，用于在数控加工中提前识别钻头卡死。研究团队发布了来自 17 台钻头的时间和频域声学特征公开数据集，并用 Leave-One-Drill-Out 验证对比了半监督 Autoencoder 与有监督 XGBoost。Autoencoder 的 Recall 达到 1.00，综合 F1-score 为 0.81，AUC 为 0.8294；XGBoost 的平均 F1-score 只有 0.55，且在部分钻头上未能检测到故障。论文还提供了 17 台钻头的公开声学数据集。
Acoustic Anomaly Detection for Early Identification of Drill Jamming
In the context of Industry 4.0, predictive maintenance is essential to minimize unplanned downtime and ensure operational safety. In machining processes, drill jamming is a critical failure that compromises both workpiece quality and tool integrity. This work proposes an acoustic anomaly detection system using ultrasonic sensors to identify pre-failure patterns in Computer Numerical Control (CNC) machining. We release a public dataset of time- and frequency-domain acoustic features from 17 drills, and benchmark semi-supervised autoencoder-based normality modelling against supervised XGBoost classification using Leave-One-Drill-Out validation. The results demonstrate that the Autoencoder is highly effective for industrial safety, achieving a Recall of 1.00 and a consolidated F1-score of 0.81. Furthermore, the threshold-independent analysis shows that the Autoencoder better separates normal and pre-failure acoustic patterns, achieving a consolidated AUC of 0.8294, compared with 0.7559 for the supervised model under inter-tool covariate shift. In contrast, the supervised XGBoost achieved an average F1-score of 0.55 and failed to detect failures in some of the evaluated drills. The findings highlight that the ultrasonic spectrum provides a reliable window for identifying incipient mechanical instabilities, and the AE model offers a robust safety envelope by anticipating all jamming events across the tested drill bits.