New Industrial Systems: Chatty Factories

新工业系统：喋喋不休的工厂

• 批准号: EP/R021031/1
• 负责人: Pete Burnap
• 金额: $ 186.97万
• 依托单位: CARDIFF UNIVERSITY
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2018
• 资助国家: 英国
• 起止时间: 2018 至 无数据
• 项目状态: 已结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FR021031%2F1
• 关键词: New; Industrial; Systems; Chatty; Factories

The first industrial revolution saw a transformation based on water and steam power. The second harnessed electricity to support mass production. The third used electronics and IT to innovate and automate. Now, the World Economic Forum defines the fourth industrial revolution as "a fusion of technologies that is blurring the lines between the physical, digital, and biological spheres". This builds on the rapid growth of the third revolution to bring data, artificial intelligence and robotic fabrication systems to the forefront of the manufacturing industry. Current manufacturing systems are characterised by a multitude of interconnected procedures that include many discrete, highly specialised and human intensive activities, such as: consumer research, concept design, engineering design & prototyping, and manufacturing operations that combine robots with human workers on the factory floor. Two significant limitations of current manufacturing systems are (a) the inability to quickly and continuously refine product design in response to real-time consumer insights (i.e. how the product is being used and its 'experience' of the world) and (b) the inability to quickly reconfigure and reskill the human and robotic production elements on the factory floor in response to real-time data captured from embedded product sensors. For example - if sensor data suggests a product needs a design change based on its current use, how do we update the fabrication instructions and reshuffle the factory floor between shifts, and tell human and robot workers how to alter their duties within minutes?Our vision for the manufacturing factory of the future is to embrace the rapid growth of internet-connected products via embedded sensors producing massive volumes of data, and transform these traditionally discrete activities into one seamless process that is capable of real-time continuous product refinement. Firstly, mindful of the potential disruption to labour markets, we will develop new fundamental theory that relates to learning and seamless communication between products, humans, robots and factory floor operations - to ensure equality and collaborative real-time learning. Secondly, we will develop data-driven systems that provide an auditable, secure and seamless flow of information between all operations inside and outside the factory to facilitate real-time adaption and re-orientation of the entire manufacturing system based on data harvested via product-embedded sensors and Internet of Things (IoT) connectivity.The research will achieve a radical interruption of the existing 'consumer sovereignty' model based around surveys and market research - and introduce 'use sovereignty' via an embedded understanding of consumer behaviour - making products that are fit for purpose based on how they are used. This will not be unmediated but buffered by robust, secure and interpretable data analysis at scale - with ethical integration of human labour. Designers will have a completely transformed role being 'embedded in production' in a world of "chatty" products and a dynamically evolving factory floor.Our approach will transform the ways in which traditional factories are reconfigured in real time by adopting a first mover approach to real time reconfiguration, production element reskilling (human, robot or both collaboratively). Radically, we will use exopedagogy, the first time it has ever been applied in a practical setting and couple this with insights from robotics. Exopedagogy considers alien forms of learning and uses those metaphors to develop models of learning which go beyond human learning. In contrast to existing techniques such as jet engine telemetry that allow for optimisation tweaks around a clearly defined product, our work will allow for both i) redesigns to support new uses or usage patterns; ii) generation of new products based on observing alternative use.

第一次工业革命见证了基于水力和蒸汽动力的变革。第二个是利用电力来支持大规模生产。第三种是利用电子和IT技术进行创新和自动化。现在，世界经济论坛将第四次工业革命定义为“模糊物理、数字和生物领域之间界限的技术融合”。这是建立在第三次革命的快速增长基础上的，将数据、人工智能和机器人制造系统带到了制造业的最前沿。当前的制造系统的特点是大量相互关联的程序，包括许多离散的、高度专业化的和人力密集型的活动，例如：消费者研究、概念设计、工程设计和原型制作，以及在工厂车间将联合收割机机器人与人类工人相结合的制造操作。当前制造系统的两个显著限制是（a）不能响应于实时消费者洞察（即，产品如何被使用及其对世界的“体验”）而快速且连续地改进产品设计，以及（B）不能响应于从嵌入式产品传感器捕获的实时数据而快速地重新配置和重新技能化工厂车间上的人类和机器人生产元件。例如，如果传感器数据表明产品需要根据其当前使用情况进行设计更改，我们如何更新制造说明并在轮班之间重新安排工厂车间，并告诉人类和机器人工人如何在几分钟内改变他们的职责？我们对未来制造工厂的愿景是通过嵌入式传感器产生大量数据来拥抱互联网连接产品的快速增长，并将这些传统上离散的活动转变为一个能够实时持续改进产品的无缝流程。首先，考虑到对劳动力市场的潜在破坏，我们将开发新的基础理论，涉及产品，人类，机器人和工厂车间操作之间的学习和无缝沟通-以确保平等和协作实时学习。其次，我们将开发数据驱动的系统，提供一个可审计的，在工厂内外的所有操作之间实现安全无缝的信息流，以促进整个制造系统基于通过产品嵌入式传感器和物联网（IoT）收集的数据进行实时调整和重新定位连通性。这项研究将彻底中断现有的基于调查和市场研究的“消费者主权”模型-并通过对消费者行为的深入理解引入“使用主权”--根据产品的使用方式来制造适合用途的产品。这将不会是无中介的，而是通过强大，安全和可解释的大规模数据分析进行缓冲-与人类劳动的道德整合。设计师将有一个完全转变的角色被“嵌入生产”在一个世界的“喋喋不休”的产品和一个动态发展的工厂flory. We的方法将改变的方式，其中传统的工厂重新配置在真实的时间，采用先行者的方法，以真实的时间重新配置，生产要素重新技能（人，机器人或两者合作）。从根本上讲，我们将使用exopedagogy，这是它第一次被应用于实际环境，并将其与机器人学的见解结合起来。探索教育学考虑了学习的外来形式，并使用这些隐喻来开发超越人类学习的学习模型。与现有的技术（如喷气发动机遥测技术）相比，这些技术允许围绕明确定义的产品进行优化调整，我们的工作将允许i）重新设计以支持新的用途或使用模式; ii）基于观察替代用途生成新产品。

项目成果

A Configurable Dependency Model of a SCADA System for Goal-Oriented Risk Assessment

• DOI: 10.3390/app12104880
• 发表时间: 2022-05
• 期刊: Applied Sciences
• 作者: Yulia Cherdantseva;P. Burnap;S. Nadjm-Tehrani;Kevin Jones

A three-tiered intrusion detection system for industrial control systems

工业控制系统的三层入侵检测系统

• DOI: 10.1093/cybsec/tyab006
• 发表时间: 2021
• 期刊: Journal of Cybersecurity
• 影响因子: 3.9
• 作者: Anthi E

Adversarial attacks on machine learning cybersecurity defences in Industrial Control Systems

• DOI: 10.1016/j.jisa.2020.102717
• 发表时间: 2021-02-02
• 期刊: JOURNAL OF INFORMATION SECURITY AND APPLICATIONS
• 影响因子: 5.6
• 作者: Anthi, Eirini;Williams, Lowri;Wedgbury, Adam
• 通讯作者: Wedgbury, Adam

Risk assessment methods for converged IoT and SCADA systems: review and recommendations

融合物联网和 SCADA 系统的风险评估方法：审查和建议

• DOI: 10.1049/cp.2019.0130
• 发表时间: 2019
• 作者: Aldmour R

Support Vector Regression for Multi-objective Parameter Estimation of Interval Type-2 Fuzzy Systems

区间2型模糊系统多目标参数估计的支持向量回归

• DOI: 10.1007/978-981-15-3290-0_8
• 发表时间: 2020
• 期刊: Advances in Intelligent Systems and Computing
• 作者: Ahmadieh Khanesar M.