EAGER: Real-D: Integrating Data-Driven Methods and Engineering Models in Manufacturing Systems

EAGER：Real-D：在制造系统中集成数据驱动方法和工程模型

• 批准号: 1839591
• 负责人: Kamran Paynabar
• 金额: $ 30万
• 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: 2018
• 资助国家: 美国
• 起止时间: 2018-09-01 至 2021-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1839591&HistoricalAwards=false
• 关键词: EAGER; Real; Integrating; Data; Driven

This EArly-concept Grant for Exploratory Research (EAGER) will contribute to national prosperity and economic welfare by studying new methods for combining machine learning with engineering knowledge to improve performance of manufacturing systems. Machine learning has attracted much attention as it offers the potential for analysis of massive data in various application domains, including engineering and manufacturing. However, the reliance on data-driven methods alone, outside of the context of engineering knowledge and physical principles, can led to misspecified models with low accuracy and/or black-box models with poor interpretability. On the other hand, engineering models generally rely on assumptions that may not hold in practice, leading to bias and poor predictive capability. This award supports fundamental research that bridges the gap between pure data-driven methods and those based purely on engineering models by introducing a novel framework that integrates statistical machine learning methods with physics, engineering and first principles to create more accurate analytical models for manufacturing systems. This research creates an analytical framework enabling a better understanding of manufacturing system performance through the fusion of data with engineering principles. This approaches is expected to improve product quality, increase machine availability and reduce manufacturing costs by identifying and controlling critical process factors. New methodologies developed in this research will be incorporated into the STEM educations curriculum and teaching activities. Going beyond existing machine learning techniques, this research will integrate data analysis and engineering modeling to provide more accurate methods for data analysis and prediction. These new methods are expected to outperform both data-driven and first principles models when they are used separately. The project will create a new sampling strategy for conducting experiments and collecting data that, unlike current design of experiment and optimal design methods, uses engineering models to guide the sampling direction and select the sampling points that most improve accuracy. The project will also build a new real-time dimension reduction and feature extraction method from streaming data that can extract both low-dimensional spatial and temporal features embedded in data streams leading to effective dimension reduction. A set of computationally efficient estimation algorithms will be developed that enable the real-time feature learning and analysis for high-velocity streams. From a quality improvement viewpoint, the project will enable researchers in the quality engineering community to reexamine quality monitoring and improvement methods with a new perspective based on the fusion of data and engineering models.This award reflects NSF's statutory mission and has been deemed worthy of support through evaluation using the Foundation's intellectual merit and broader impacts review criteria.

这项对探索性研究的早期概念赠款（急切）将通过研究将机器学习与工程知识相结合以提高制造系统绩效的新方法，从而为国家的繁荣和经济福利做出贡献。机器学习引起了很多关注，因为它为分析包括工程和制造在内的各种应用领域中的大量数据提供了潜力。但是，仅在工程知识和物理原理的背景下，仅对数据驱动的方法的依赖可能导致具有较低准确性和/或黑盒模型的伪造模型，并且可解释性差。另一方面，工程模型通常依赖于在实践中可能不存在的假设，从而导致偏见和预测能力差。该奖项支持基础研究，通过引入一个新颖的框架，将统计机器学习方法与物理，工程和第一原则集成，以创建制造系统的分析模型，从而弥合了纯数据驱动方法与纯粹基于工程模型的方法之间的差距。这项研究创建了一个分析框架，从而通过将数据与工程原理融合来更好地了解制造系统的性能。 预计这种方法将通过识别和控制关键过程因素来提高产品质量，提高机器的可用性并降低制造成本。这项研究中开发的新方法将纳入STEM教育课程和教学活动。超越了现有的机器学习技术，这项研究将整合数据分析和工程建模，以提供更准确的数据分析和预测方法。 当这些新方法单独使用时，预计它们的表现将胜过数据驱动和第一原理模型。 该项目将创建一种新的采样策略，用于进行实验和收集数据，与当前的实验和最佳设计方法设计不同，该策略使用工程模型来指导采样方向并选择最大程度提高准确性的采样点。该项目还将从流数据中构建一个新的实时维度减少和特征提取方法，该方法可以提取嵌入在数据流中的低维空间和时间特征，从而导致有效的尺寸降低。将开发一组计算高效的估计算法，以实现高速流的实时特征学习和分析。从质量改进的角度来看，该项目将使质量工程社区的研究人员能够基于数据和工程模型的融合，重新检查质量监测和改进方法。该奖项反映了NSF的法定任务，并被认为是通过基金会的知识分子的智力和更广泛的影响来评估的，并被认为是值得的。

项目成果

Functional directed graphical models and applications in root-cause analysis and diagnosis

• DOI: 10.1080/00224065.2020.1805380
• 发表时间: 2020-08-23
• 期刊: JOURNAL OF QUALITY TECHNOLOGY
• 影响因子: 2.5
• 作者: Gomez, Ana Maria Estrada;Paynabar, Kamran;Pacella, Massimo
• 通讯作者: Pacella, Massimo

Multi-sensor prognostics modeling for applications with highly incomplete signals

• DOI: 10.1080/24725854.2020.1789779
• 发表时间: 2020-07
• 期刊: IISE Transactions
• 影响因子: 2.6
• 作者: Xiaolei Fang;Hao Yan;N. Gebraeel;K. Paynabar

Accelerating Parallel Hierarchical Matrix-Vector Products via Data-Driven Sampling

• DOI: 10.1109/ipdps47924.2020.00082
• 发表时间: 2020-05
• 期刊: 2020 IEEE International Parallel and Distributed Processing Symposium (IPDPS)
• 作者: Lucas Erlandson;Difeng Cai;Yuanzhe Xi;Edmond Chow