RAPID: ReAl-time Process ModellIng and Diagnostics: Powering Digital Factories

RAPID：实时过程建模和诊断：为数字工厂提供动力

• 批准号: EP/V028618/1
• 负责人: Nicholas Polydorides
• 金额: $ 53.8万
• 依托单位: University of Edinburgh
• 依托单位国家: 英国
• 项目类别: Research Grant
• 财政年份: 2022
• 资助国家: 英国
• 起止时间: 2022 至 无数据
• 项目状态: 未结题
• 来源: https://gtr.ukri.org/projects?ref=EP%2FV028618%2F1
• 关键词: RAPID; ReAl; time; Process; ModellIng

Modern manufacturing involves highly controlled and automated processes meticulously designed to deliver products to certain needs within strict specifications and in a cost-efficient and sustainable way. To ensure performance in variable and often harsh conditions, sensors capture continuous data streams about the state of the process, e.g. equipment, and the product. The ability to analyse this data in real time, however, offers unique advantages that are currently out of reach. Learning to calibrate its operation from sensor data, monitor its health status and make accurate forecasts on product outcomes and maintenance requirements, are process attributes of future autonomous factories. We have teamed up with Seagate, a major manufacturer of hard drives, to design, develop and implement such a technology in their factory, GSK, a leading pharmaceutical manufacturer, who have recognised the potential of real-time process analytics and NVIDIA, the global GPU provider. The goal is to establish a level of production robustness against major disruptions and market volatility that create uncertainties on workforce numbers and supply chain continuity. This vision paves the way for responsive manufacturing systems and digitally controlled factories but to materialise technology that can seamlessly analyse sensor-obtained data and translate it to actionable information. Whilst companies capture large datasets, their ability to process them and react in real-time, is hindered by the algorithms' complexity and scale of the data. Indeed, if anything, the current pandemic has reinforced the need to enhance manufacturing capability to cope with sudden increases in demand, production repurposing, and possibly even unmanned, autonomous production. A step change is needed in the processing capability and manufacturing systems where the data can be analysed in real time at the edge, i.e. on the factory floor, making it secure and thus ensuring more effective performance by being less reliant on external communications and high-performance processing resources. We propose that this be done in a methodological and secure way with minimal dependencies on external factors, thus prompting us to investigate ways of performing real-time analytics in a practical, cost-effective and sustainable manner. RAPID proposes a two-pronged approach to reduce the computational dimensionality through novel 'data sketching' algorithms and optimisation using 'transprecision computing' on GPU technology to provide further acceleration. In detailed interactions with Seagate and GSK both based in the UK, we have identified manufacturing stages where real-time analytics can play a major part in transforming processes and outcomes. In particular, the proposed technology will be applied to a 'diagnostic analytics' case study involving optical imaging data for a critical metrology stage in disk manufacture and two 'predictive analytics' examples for model learning to predict the health state of silicon wafers and for improved fault detection, feature extraction and monitoring of chemical products. Data sketching dramatically reduces the complexity of computations by randomly sampling few, the most informative, data and model entries leading to small-scale computations that can be performed very quickly with a small compromise on precision. Sketching trades off precision and speed, and if done optimally a two-order of magnitude speedup is feasible, when sampling around 10% of the data. To exploit this advantage further, the sketched computations are implemented using transprecision computing that challenges traditional computing to further accelerate computations when high precision is not required. In computing with noisy data and learned statistical models in factory environments, a controllable reduction in precision is prudent for performance improvement but also essential for noise robustness.

现代制造业涉及高度受控和自动化的过程，精心设计，在严格的规格内以成本效益和可持续的方式提供产品以满足特定需求。为了确保在多变且往往恶劣的条件下的性能，传感器捕获有关过程状态的连续数据流，例如设备和产品。然而，实时分析这些数据的能力提供了目前无法企及的独特优势。学会根据传感器数据校准其操作，监控其健康状态，并对产品结果和维护要求做出准确预测，是未来自治工厂的流程属性。我们已经与主要的硬盘制造商希捷合作，在他们的工厂设计、开发和实施这样的技术，GSK是一家领先的制药制造商，他们已经认识到实时过程分析的潜力，并在全球GPU供应商NVIDIA。其目标是建立一个针对重大中断和市场波动的生产稳健性水平，这些中断和市场波动会给劳动力数量和供应链连续性带来不确定性。这一愿景为响应式制造系统和数字控制工厂铺平了道路，但也实现了能够无缝分析传感器获取的数据并将其转化为可操作信息的技术。虽然公司捕获大数据集，但算法的复杂性和数据的规模阻碍了它们处理这些数据并做出实时反应的能力。事实上，如果说有什么不同的话，那就是当前的大流行强化了提高制造能力的必要性，以应对需求的突然增加、生产用途的改变，甚至可能是无人值守的自主生产。需要逐步改变处理能力和制造系统，使数据可以在边缘，即在工厂车间进行实时分析，使其安全，从而通过减少对外部通信和高性能处理资源的依赖来确保更有效的性能。我们建议以一种方法和安全的方式完成这项工作，最大限度地减少对外部因素的依赖，从而促使我们研究以实用、成本效益和可持续的方式执行实时分析的方法。Rapid提出了一种双管齐下的方法来降低计算维度，方法是通过新颖的“数据草图”算法和基于GPU技术的“变换计算”进行优化，以提供进一步的加速。在与总部位于英国的希捷和葛兰素史克的详细互动中，我们确定了制造阶段，在这些阶段，实时分析可以在转变过程和结果方面发挥重要作用。特别是，建议的技术将被应用于一个“诊断分析”案例研究，该案例研究涉及光盘制造中关键计量阶段的光学成像数据，以及两个“预测分析”示例，用于模型学习以预测硅片的健康状态，以及改进对化学产品的故障检测、特征提取和监控。数据草图通过随机抽样少数几个信息量最大的数据和模型条目，从而极大地降低了计算的复杂性，从而导致小规模计算，这些计算可以非常快速地执行，但精度略有下降。绘制草图在精度和速度之间进行了权衡，如果操作得当，在采样约10%的数据时，加速两个数量级是可行的。为了进一步利用这一优势，草图计算使用传输精度计算来实现，这对传统计算提出了挑战，在不需要高精度的情况下进一步加速计算。在工厂环境中使用噪声数据和学习的统计模型进行计算时，可控的精度降低对于性能改进是谨慎的，但对于噪声稳健性也是必不可少的。

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