基于矩阵轮廓的无监督方法，精准识别增材制造中的隐性异常
An Unsupervised Time Series Anomaly Detection Approach for Efficient Online Process Monitoring of Additive Manufacturing
- 利用矩阵轮廓捕捉制造周期相似性，实现无监督异常检测
- 可发现细微语义偏差，准确识别缺陷发生的初始时刻
- 适合缺乏标注数据的工业在线监测场景
在线传感在现代制造中至关重要，实时传感器信号以高分辨率时间序列形式存储，蕴含丰富的运行状态信息。其常见应用之一是在线过程监控，可通过从传感器信号中有效检测异常来实现。然而，现有方法大多依赖标签数据训练监督模型，或仅能检测极端离群点，难以识别细微的语义偏移，无法捕捉新工况或意外流程的起始。为此，本文提出一种基于矩阵轮廓的无监督异常检测算法，通过捕捉制造周期的相似性并进行语义分割，精确识别增材制造中缺陷异常的开始时刻。实验在真实传感器数据上验证了该方法的有效性。
原文摘要 · Abstract (English)
Online sensing plays an important role in advancing modern manufacturing. The real-time sensor signals, which can be stored as high-resolution time series data, contain rich information about the operation status. One of its popular usages is online process monitoring, which can be achieved by effective anomaly detection from the sensor signals. However, most existing approaches either heavily rely on labeled data for training supervised models, or are designed to detect only extreme outliers, thus are ineffective at identifying subtle semantic off-track anomalies to capture where new regimes or unexpected routines start. To address this challenge, we propose an matrix profile-based unsupervised anomaly detection algorithm that captures fabrication cycle similarity and performs semantic segmentation to precisely identify the onset of defect anomalies in additive manufacturing. The effectiveness of the proposed method is demonstrated by the experiments on real-world sensor data.
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