Multiple event identification and characterization by retrospective analysis of structured data streams
Multiple event identification and characterization by retrospective analysis of structured data streams
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DOI：
10.1080/24725854.2021.1970863
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发表时间：
2021-11
影响因子：
2.6
通讯作者：
Andi Wang;Tzyy-Shuh Chang;Jianjun Shi
中科院分区：
文献类型：
--
作者：
Andi Wang;Tzyy-Shuh Chang;Jianjun Shi
摘要复杂系统中的传感器产生大量的数据，这些数据包含有关系统运行状态的丰富信息。本文提出了一种历史数据集的回顾性分析方法，该方法同时识别系统何时发生多个事件，并表征它们如何影响多个传感信号。问题的制定是出于字典学习方法和解决方案是通过迭代更新的事件签名和序列，使用ADMM算法。仿真研究和轧钢过程的案例研究验证了我们的方法。补充材料，包括附录和复制报告，可在网上查阅。
Abstract The sensors installed in complex systems generate massive amounts of data, which contain rich information about a system’s operational status. This article proposes a retrospective analysis method for a historical data set, which simultaneously identifies when multiple events occur to the system and characterizes how they affect the multiple sensing signals. The problem formulation is motivated by the dictionary learning method and the solution is obtained by iteratively updating the event signatures and sequences using ADMM algorithms. A simulation study and a case study of the steel rolling process validate our approach. The supplementary materials including the appendices and the reproduction report are available online.