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

摘要王/何，1547163/1547124（合作提案）人们普遍认为，连接到互联网的工厂和工厂比未连接互联网的工厂和工厂更有效率，生产力更高，更智能。 下一代制造系统预计将包括越来越强大和低成本的计算和网络化的信息为基础的技术的应用。 物联网（IoT）设备是传感器/执行器，具有小型且易于嵌入的无线网络的计算机。物联网设备为使用大量传感器的仪器系统提供了机会。随着物联网设备的巨大数据量和可编程性，有机会塑造接收到的数据，解决信息的本地冗余，并提高本地和分布式参数系统（如反应堆）测量的准确性和精度。随着物联网的出现，不断提高的计算能力和无线网络技术的扩展，新一代的网络，基于信息的技术、数据分析和预测建模正在提供新的嵌入式计算能力以及对数据和信息的以前无法想象的潜在用途的访问。这些能力为新的、从根本上更好的制造方式提供了可能性。如先进制造合作伙伴2.0（AMP 2.0）中所述，如果在早期检测到潜在故障和故障并进行纠正，则可以实现五年内将工厂停机时间减少50%，十年内减少90%。将这些可能性转化为现实仍然具有挑战性。在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诊断。作为这项工作的一部分，他们还建议建立一个物联网支持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过程监控（SPPM）框架作为潜在的解决方案之一。该SPPM将利用之前未使用的过程变量的高阶统计量来直接量化过程非线性和非正态性。此外，提出了一种基于贝叶斯的事件分类，使智能，自适应建模，网络制造系统监控的关键能力。通过建立一个物联网支持的制造技术测试平台并运行设计的实验，该项目应能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。 所提出的基于统计模式的数据分析的思想不限于过程监控。它提供了一个建模框架，可以通过将网络制造大数据与不同的目标相关联，应用于流程/产品设计和预测性维护。

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.