Real-time detection of clustered events in video-imaging data with applications to additive manufacturing
Real-time detection of clustered events in video-imaging data with applications to additive manufacturing
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DOI：
10.1080/24725854.2021.1882013
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发表时间：
2021-02-18
影响因子：
2.6
通讯作者：
Colosimo, Bianca Maria
中科院分区：
文献类型：
--
作者：
Yan, Hao;Grasso, Marco;Colosimo, Bianca Maria
将视频成像数据用于在线过程监控应用在工业中已变得流行起来。在这个框架中，需要时空统计过程监测方法来捕获相关信息内容并发出可能失控状态的信号。视频成像数据的特征是依赖于潜在现象的时空可变性结构，并且典型的失控模式与在时间和空间上都定位的事件有关。在本文中，我们提出了一种时空分解和回归相结合的视频数据异常检测方法。失控事件通常是稀疏的，在空间上聚集，在时间上一致。目标不仅是尽快发现异常(“何时”)，而且还要在太空中(“何处”)定位它。该方法将原始时空数据分解为随机的自然事件、稀疏的空间聚集和时间一致的异常事件以及随机噪声。给出了时空回归的递归估计过程，以使所提出的方法能够实时实施。最后，提出了一种似然比检验方法来检测异常发生的时间和地点。将该方法应用于高速视频成像数据的分析，以检测和定位金属添加剂制造过程中的局部热点。
The use of video-imaging data for in-line process monitoring applications has become popular in industry. In this framework, spatio-temporal statistical process monitoring methods are needed to capture the relevant information content and signal possible out-of-control states. Video-imaging data are characterized by a spatio-temporal variability structure that depends on the underlying phenomenon, and typical out-of-control patterns are related to events that are localized both in time and space. In this article, we propose an integrated spatio-temporal decomposition and regression approach for anomaly detection in video-imaging data. Out-of-control events are typically sparse, spatially clustered and temporally consistent. The goal is not only to detect the anomaly as quickly as possible ("when") but also to locate it in space ("where"). The proposed approach works by decomposing the original spatio-temporal data into random natural events, sparse spatially clustered and temporally consistent anomalous events, and random noise. Recursive estimation procedures for spatio-temporal regression are presented to enable the real-time implementation of the proposed methodology. Finally, a likelihood ratio test procedure is proposed to detect when and where the anomaly happens. The proposed approach was applied to the analysis of high-sped video-imaging data to detect and locate local hot-spots during a metal additive manufacturing process.