Abstract

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.

摘要

将视频成像数据用于在线过程监控应用已在工业中流行起来。在这个框架中，需要时空统计过程监控方法来捕捉相关的信息内容和信号可能的失控状态。视频成像数据的特点是时空变化结构取决于潜在现象，典型的失控模式与时间和空间上的局部事件有关。在本文中，我们提出了一种集成的时空分解和回归方法，用于视频成像数据中的异常检测。失控事件通常是稀疏的、空间聚集的和时间一致的。目标不仅是尽可能快地检测到异常（“何时”），而且还要在空间中定位它（“哪里”）。所提出的方法通过将原始时空数据分解为随机自然事件、稀疏空间聚集和时间一致的异常事件以及随机噪声来工作。提出了时空回归的递归估计程序，以实现所提出的方法的实时实施。最后，提出了一种似然比测试程序来检测异常发生的时间和地点。所提出的方法被应用于高速视频成像数据的分析，以检测和定位金属增材制造过程中的局部热点。提出了时空回归的递归估计程序，以实现所提出的方法的实时实施。最后，提出了一种似然比测试程序来检测异常发生的时间和地点。所提出的方法被应用于高速视频成像数据的分析，以检测和定位金属增材制造过程中的局部热点。提出了时空回归的递归估计程序，以实现所提出的方法的实时实施。最后，提出了一种似然比测试程序来检测异常发生的时间和地点。所提出的方法被应用于高速视频成像数据的分析，以检测和定位金属增材制造过程中的局部热点。