---
title: "Acoustic Anomaly Detection for Early Identification of Drill Jamming"
source: "arXiv AI/CL/LG（论文）"
source_tier: "t2"
url: "https://arxiv.org/abs/2611.07356v1"
permalink: "https://feed.astralor.com/items/itm_dd07398b4ae6b08f9e401092"
published: "2026-11-05T02:50:29.000Z"
score: 58
channel: "tech"
tags: ["安全","RAG","Acoustic Anomaly Detection","Early Identification","Drill Jamming","Industry","Computer Numerical Control","CNC"]
---

# Acoustic Anomaly Detection for Early Identification of Drill Jamming

## At a glance

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 workpie…

## Key points

- 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.

## Full text

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.

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