EAGER:Cybermanufacturing:Collaborative Research: A novel process data analytics framework for IoT-enabled cybermanufacturing

EAGER：网络制造：协作研究：用于物联网网络制造的新型过程数据分析框架

• 批准号: 1547163
• 负责人: Jin Wang
• 金额: $ 24.49万
• 依托单位: Auburn University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2015
• 资助国家: 美国
• 起止时间: 2015-09-15 至 2018-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=1547163&HistoricalAwards=false
• 关键词: EAGER; Cybermanufacturing; Collaborative; Research; novel

Abstract Wang/He, 1547163/1547124 (Collaborative proposal)There is general consensus that factories and plants that are connected to the internet are more efficient, productive and smarter than their non-connected counterparts. Next generation manufacturing systems are expected to include the application of increasingly powerful and low-cost computation and networked information-based technologies. Internet of Things (IoT) devices are sensors/actuators, computers with wireless networks that are small and easy to embed. IoT devices offer the opportunity to instrument systems with massive numbers of sensors. With the huge amount of data and the programmability of IoT devices, comes the opportunity to shape the data received, to address local redundancy of information, and to improve both the accuracy and precision of measurements locally and across a distributed parameter system such as a reactor.With the emergence of the IoT and ever advancing computing power and expansion of wireless networking technologies, a new generation of networked, information-based technologies, data analytics, and predictive modeling are providing new embedded computing capabilities as well as access to previously unimagined potential uses of data and information. These capabilities provide possibilities for new, radically better ways of doing manufacturing. As noted in Advanced Manufacturing Partnership 2.0 (AMP 2.0), if potential faults and failures are detected and corrected while still incipient, reduction of plant downtimes of 50% in five years and 90% in ten years may be achieved. Converting these possibilities into reality remains challenging. In this EAGER proposal, the PIs propose a new process data analytics framework with the aim of providing smart diagnostics and prognostics for cybermanufacturing. As part of this effort, they also propose to establish an IoT-enabled manufacturing technology testbed (MTT) to explore and establish a proof-of-concept for the proposed framework.The PIs propose a statistics pattern based process monitoring (SPPM) framework as one of the potential solutions. This SPPM will make use of the higher order statistics of process variables that have not been utilized before to directly quantify the process nonlinearity and nonnormality. In addition, a Bayesian-based event classification is proposed to enable intelligent, self-adaptive modeling, a key capability of cybermanufacturing system monitoring. By establishing an IoT enabled manufacturing technology testbed and running designed experiments, this project should yield a better understanding of the properties, capacities and performances of IoT devices. If successful, this project will create one of the first prototypes of the 3rd generation statistical process monitoring methods. The idea of the proposed statistics pattern based data analytics is not limited to process monitoring. It provides a modeling framework that can be applied for process/product design and predictive maintenance by relating cybermanufacturing Big Data to different objectives.

【摘要】Wang/He， 1547163/1547124（协作提案）人们普遍认为，连接到互联网的工厂和工厂比未连接的工厂和工厂更高效、更高效、更智能。下一代制造系统预计将包括应用日益强大和低成本的计算和网络化信息技术。物联网（IoT）设备是传感器/执行器，具有无线网络的计算机，体积小且易于嵌入。物联网设备为具有大量传感器的仪表系统提供了机会。随着大量数据和物联网设备的可编程性，有机会塑造接收到的数据，解决本地信息冗余，并提高本地和跨分布式参数系统（如反应堆）测量的准确性和精度。随着物联网的出现和不断进步的计算能力以及无线网络技术的扩展，新一代网络化、信息化技术、数据分析和预测建模正在提供新的嵌入式计算能力，以及访问以前无法想象的数据和信息的潜在用途。这些能力为新的、从根本上更好的制造方式提供了可能性。正如先进制造伙伴关系2.0 （AMP 2.0）中所指出的，如果潜在的故障和故障在初期就被发现和纠正，那么工厂的停机时间在5年内减少50%，在10年内减少90%。将这些可能性变为现实仍然具有挑战性。在这个EAGER提案中，pi提出了一个新的过程数据分析框架，旨在为网络制造提供智能诊断和预测。作为这项工作的一部分，他们还提议建立一个支持物联网的制造技术测试平台（MTT），以探索和建立拟议框架的概念验证。pi提出了一个基于统计模式的流程监控（SPPM）框架作为潜在的解决方案之一。该SPPM将利用以前未使用的过程变量的高阶统计量来直接量化过程的非线性和非正态性。此外，提出了一种基于贝叶斯的事件分类，以实现智能、自适应建模，这是网络制造系统监控的关键能力。通过建立一个支持物联网的制造技术测试平台并运行设计的实验，该项目将更好地了解物联网设备的属性、容量和性能。如果成功，该项目将创建第三代统计过程监控方法的首批原型之一。所建议的基于数据分析的统计模式的思想并不局限于流程监控。它提供了一个建模框架，通过将网络制造大数据与不同的目标联系起来，可以应用于流程/产品设计和预测性维护。