Collaborative Research: Online Monitoring of High-Dimensional Streaming Data Using Adaptive Order Shrinkage

合作研究：利用自适应阶次收缩在线监测高维流数据

• 批准号: 1362876
• 负责人: Yajun Mei
• 金额: $ 22.43万
• 依托单位: Georgia Tech Research Corporation
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2014
• 资助国家: 美国
• 起止时间: 2014-08-01 至 2018-07-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1362876&HistoricalAwards=false
• 关键词: Collaborative; Research; Online; Monitoring; Dimensional

Modern manufacturing machines and systems incorporate sensors to monitor process conditions, but the massive amount of data coming from them is often difficult to interpret. This project investigates a new method for extracting useful information from such data. The method potentially can reduce computational cost and improve confidence in the predictions that are made by adapting statistical process control (SPC) methodologies specifically for online monitoring of high-dimensional streaming data. While this work focuses on monitoring the Chemical Mechanical Planarization (CMP) process, the methods can also be applied to other manufacturing applications, such as rolling, forging and casting processes. The impacts of the methodologies will go beyond manufacturing, including but not limited to disease surveillance in epidemiology, network traffic control, intrusion detection and surveillance video. The success of the implementation of the research methods will not only generate significant economic impacts to the nation, but also prevent consequent damages through quick detection of abnormal events. In addition, the education plan will make broad impacts on the workforce training through curriculum and lab developments, teaching innovations, and other outreach activities.The objective of this collaborative research is to develop scalable and adaptive methodologies for online monitoring of high-dimensional streaming data. In particular, three interrelated research tasks are planned in the methodology development: (1) Efficient scalable schemes via adaptive order shrinkage with full observations, and the key novel idea is to first monitor each data stream locally through some classical, computationally simple, but efficient local detection statistics, and then combine these local procedures ?smartly? to produce a single global monitoring scheme; (2) Adaptive sampling strategies over the spatial domain, rather than the conventional time domain, such that the most informative data streams are actively selected/sampled to maximize the sensitivity and effectiveness for change detection with consideration of resources constraints; and, (3) The engineering knowledge enhanced monitoring scheme that integrates domain knowledge with local detection statistics development and adaptive sampling strategy to further improve performance. The success of this research will advance the state of the art in statistical process control and contribute to the science base of quality improvement for manufacturing systems.

现代制造机器和系统采用传感器来监控过程条件，但来自它们的大量数据往往难以解释。 该项目研究一种从此类数据中提取有用信息的新方法。 该方法潜在地可以降低计算成本并提高预测的置信度，所述预测是通过调整统计过程控制（SPC）方法专门用于高维流数据的在线监测而做出的。虽然这项工作的重点是监测化学机械平坦化（CMP）过程，但这些方法也可以应用于其他制造应用，如轧制，锻造和铸造工艺。这些方法的影响将超越制造业，包括但不限于流行病学中的疾病监测、网络流量控制、入侵检测和监控视频。研究方法的成功实施不仅会对国家产生重大的经济影响，而且还可以通过快速检测异常事件来防止随之而来的损害。此外，教育计划将通过课程和实验室开发，教学创新和其他推广活动对劳动力培训产生广泛影响。这项合作研究的目标是开发可扩展和自适应的方法，用于在线监测高维流数据。特别是，三个相互关联的研究任务，计划在方法的发展：（1）有效的可扩展计划，通过自适应订单收缩与充分的意见，和关键的新想法是首先监测每个数据流本地通过一些经典的，计算简单，但有效的本地检测统计，然后联合收割机这些本地程序？聪明地？（2）在空间域而不是传统的时域上的自适应采样策略，使得在考虑资源约束的情况下主动地选择/采样信息量最大的数据流以最大化变化检测的灵敏度和有效性;（3）工程知识增强的监测方案，将领域知识与局部检测统计开发和自适应采样策略相结合，以进一步提高性能。本研究的成功将推动统计过程控制的发展，并为制造系统质量改进提供科学基础。

项目成果

Improved performance properties of the CISPRT algorithm for distributed sequential detection

改进了用于分布式顺序检测的 CISPRT 算法的性能特性

• DOI: 10.1016/j.sigpro.2020.107573
• 发表时间: 2020
• 期刊: Signal Processing
• 影响因子: 4.4
• 作者: Liu, Kun;Mei, Yajun
• 通讯作者: Mei, Yajun

Tandem-width sequential confidence intervals for a Bernoulli proportion

伯努利比例的串联宽度连续置信区间

• DOI: 10.1080/07474946.2019.1611315
• 发表时间: 2019
• 期刊: Sequential Analysis
• 作者: Yaacoub, Tony;Goldsman, David;Mei, Yajun;Moustakides, George V.
• 通讯作者: Moustakides, George V.