GOALI: Next generation feature-based process monitoring for smart manufacturing

GOALI：下一代基于特征的智能制造过程监控

基本信息

批准号：
1805950
负责人：
QINGHUA HE
金额：
$ 32万
依托单位：
Auburn University
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 2024-08-31
项目状态：
已结题

来源：
https://www.nsf.gov/awardsearch/showAward?AWD_ID=1805950&HistoricalAwards=false
关键词：
GOALI Next generation feature based

项目摘要

The goal of process monitoring is to detect the onset and identify the underlying reasons that can cause a manufacturing environment to deviate from its desired operation. If potential faults and failures are detected and corrected while still incipient, reduction in plant downtimes of up to 50% in five years and up to 90% in ten years can be achieved. However, achieving these targets remains very challenging, as the current state-of-the-art process monitoring solutions have limitations in addressing, for example, the process dynamics and nonlinear of advanced manufacturing processes. The big data sets that are generated from smart manufacturing processes pose additional challenges. In this project a research team from Auburn University and Praxair will develop and validate a next-generation feature-based statistical process monitoring (SPM) framework as an effective way to address current challenges in process monitoring.The proposed project will systematically examine the underlying connections between various features and process characteristics, which will lay the foundation for the proposed feature-based SPM framework. With the industrial Internet-of-things (IIoT) still in its infancy, the research team aspires to develop lab scale IIoT-enabled manufacturing technology testbeds (MTT), which will allow detailed understanding of the dynamic behavior of IIoT sensors, and simulation models to accurately capture the behavior of IIoT sensors. The team plans to develop a suite of simulated IIoT-enabled MTTs and a comprehensive feature library, the associated decision tree to guide the relevant feature identification, and the automated feature selection algorithm to complement the feature-based SPM framework. The suite of IIoT-enabled MTT simulators and the feature library will be made publicly available in the form of open source codes. The proposed feature-based monitoring methodology can be extended to other areas, such as feature-based control, feature-based optimization and feature-based predictive maintenance. The proposed educational and outreach efforts focus on preparing students for careers in advanced manufacturing and providing research opportunities to minorities.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.

过程监测的目的是检测发作并确定可能导致制造环境偏离其所需操作的根本原因。如果在仍处于初期的同时检测并纠正了潜在的故障和故障，则可以在五年内降低高达50％的工厂下降，而在十年内可以减少多达90％。但是，实现这些目标仍然非常具有挑战性，因为当前的最新过程监视解决方案在解决高级制造过程的过程动态和非线性方面存在局限性。由智能制造过程产生的大数据集提出了其他挑战。在这个项目中，来自奥本大学和Praxair的研究团队将开发和验证下一代基于功能的统计过程监控（SPM）框架，作为解决过程监控中当前挑战的有效方法。拟议的项目将系统地研究各种功能和过程特征之间的基本联系，这将为拟议的基于特征SPM框架提供基础。随着工业互联网（IIOT）仍处于起步阶段，研究小组渴望开发实验室规模的制造技术测试台（MTT），这将允许对IIOT传感器的动态行为进行详细了解，并模拟模型准确捕获IIOT传感器的行为。该团队计划开发一套模拟IIOT启用的MTT和一个全面的功能库，相关的决策树以指导相关功能识别以及自动化功能选择算法，以补充基于功能的SPM框架。启用了IIOT的MTT模拟器和功能库的套件将以开源代码的形式公开提供。提出的基于功能的监视方法可以扩展到其他领域，例如基于功能的控制，基于功能的优化和基于功能的预测性维护。拟议的教育和外展工作着重于为学生提供高级制造业的职业并向少数群体提供研究机会。该奖项反映了NSF的法定任务，并被认为是值得通过基金会的知识分子优点和更广泛的影响评估的评估来支持的。