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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.

过程监控的目标是检测可能导致制造环境偏离其预期操作的起始点并识别潜在原因。如果能够在故障发生初期就检测到并纠正潜在的故障和失效，则可以在五年内将工厂停机时间减少50%，在十年内减少90%。然而，实现这些目标仍然非常具有挑战性，因为当前最先进的过程监控解决方案在解决例如先进制造过程的过程动态和非线性方面具有局限性。智能制造过程中产生的大数据集带来了额外的挑战。在本项目中，来自奥本大学和普莱克斯的研究团队将开发和验证下一代基于特征的统计过程监控（SPM）框架，作为解决当前过程监控挑战的有效方法。拟议的项目将系统地研究各种特征和过程特性之间的潜在联系，这将为拟议的基于特征的SPM框架奠定基础。由于工业物联网（IIoT）仍处于起步阶段，研究团队渴望开发实验室规模的支持IIoT的制造技术测试床（MTT），这将有助于详细了解IIoT传感器的动态行为，并建立仿真模型来准确地捕获IIoT传感器的行为。该团队计划开发一套模拟的IIoT支持的MT和一个全面的特征库，相关的决策树来指导相关的特征识别，以及自动化特征选择算法来补充基于特征的SPM框架。支持IIoT的MTT模拟器套件和功能库将以开源代码的形式公开提供。所提出的基于特征的监测方法可以扩展到其他领域，如基于特征的控制，基于特征的优化和基于特征的预测性维护。拟议的教育和推广工作的重点是为学生在先进制造业的职业生涯做好准备，并为少数民族提供研究机会。该奖项反映了NSF的法定使命，并被认为值得通过使用基金会的智力价值和更广泛的影响审查标准进行评估。