实时检测视频成像数据中的干扰事件并应用于增材制造
Real-time Detection of Clustered Events in Video-imaging data with Applications to Additive Manufacturing
摘要：
视频成像数据用于在线过程监控应用在工业中变得越来越流行。在这个框架中，时空统计过程监测方法需要捕捉相关的信息内容和信号可能失控的状态。视频成像数据的特点是由一个时空变异性结构，取决于潜在的现象，和典型的失控模式有关的事件，在时间和空间上都是本地化的。在本文中，我们提出了一个集成的时空分解和回归方法的异常检测视频成像数据。失控事件通常是稀疏的空间聚集和时间一致的。因此，目标不仅是尽可能快地检测异常（“何时”），而且还要定位它（“何处”）。该方法通过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。时空回归的递归估计程序，使所提出的方法的实时实施。最后，提出了一种似然比检验方法来检测热点的发生时间和位置。所提出的方法被应用于视频成像数据的分析，以检测和定位在金属增材制造过程中的逐层过程期间的局部过热现象（“热点”）。
英文摘要：
The use of video-imaging data for in-line process monitoring applications has become more and more popular in the 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 the events that are localized both in time and space. In this paper, 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. Therefore, the goal is to not only detect the anomaly as quickly as possible ("when") but also locate it ("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 hotspot happens. The proposed approach was applied to the analysis of video-imaging data to detect and locate local over-heating phenomena ("hotspots") during the layer-wise process in a metal additive manufacturing process.
机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。
