I-Corps: AI-enabled automation intelligence software that can detect micro-anomalies in machine and robotic operations

I-Corps：支持人工智能的自动化智能软件，可以检测机器和机器人操作中的微异常

• 批准号: 2054691
• 负责人: Jeremy Rickli
• 金额: $ 5万
• 依托单位: Wayne State University
• 依托单位国家: 美国
• 项目类别: Standard Grant
• 财政年份: 2021
• 资助国家: 美国
• 起止时间: 2021-01-15 至 2023-06-30
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2054691&HistoricalAwards=false
• 关键词: Corps; AI; enabled; automation; intelligence

The broader impact/commercial potential of this I-Corps projects is the development of an Artificial Intelligence (AI)-enabled automation software that detects micro-anomalies in machine and robotic operations and pinpoints defective components before catastrophic equipment failure. Defective components cause unscheduled downtime and are a significant challenge for manufacturers. In a typical factory, an engineer may spend 30-60 percent of their time collecting information related to defective parts. Eighty-two percent of companies have experienced unplanned downtime that costs an average of $2 million annually. However, 70% of manufacturers still lack awareness of when equipment assets are due for maintenance or upgrade, and 72% of manufacturers identify unplanned downtime as their top priority or a high priority. The high penetration level of advanced automation and sensor technologies in industrial operations also has increased demand for methods to effectively monitor and manage complex operations in a systematic and timely manner. The proposed technology addresses this need by delivering capabilities to maintenance and industrial engineers to achieve near zero Mean Time-to-Resolution (MTTR) of costly manufacturing equipment failures. In addition, the technology eliminates the machine failures from occurring by advancing approaches that analyze data and provide insights after failure occurs. This I-Corps project is based on the development of 1) a proprietary Programmable Logic Controller (PLC) driver that captures machine component micro degradation trends; 2) digital twins of the machine ecosystem that mirror the physical system performance and interactions between machines; and 3) an Automation Intelligence network. The proposed technology’s unique machine performance datasets, created from its PLC-driver and sensor feedback, captures the “normal” operation of the machines. These datasets are then used to create causal relationship maps between different elements of the system. Causal maps accelerate the detection of root causes of potential machine failure and anticipate the potential risks and weaknesses within a system. In addition, digital twins provide a cyber-physical platform that integrates physics based models of machine components, spatial relationships from Computer Aided Engineering (CAE) models, and data-driven correlations inferred from self-adapting machine learning methods. The AI platform runs on a distributed system/edge analytics network to ensure real-time monitoring and diagnosis on the local node (e.g., individual machines and its components) and system (e.g., production lines) levels. Integration of these three elements establishes a scalable digital twin that leverages element interactions to continuously discover and refine causal relationships that rapidly and reliably detect, identify, and pinpoint issues.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.

这个I-Corps项目更广泛的影响/商业潜力是开发一种支持人工智能（AI）的自动化软件，该软件可以检测机器和机器人操作中的微异常，并在灾难性设备故障之前精确定位有缺陷的组件。有缺陷的组件会导致计划外的停机时间，这对制造商来说是一个重大挑战。在一个典型的工厂里，工程师可能会花费30%-60%的时间来收集与缺陷零件相关的信息。 82%的公司经历过计划外停机，平均每年损失200万美元。然而，70%的制造商仍然缺乏对设备资产何时需要维护或升级的意识，72%的制造商将计划外停机视为其首要任务或高度优先事项。先进的自动化和传感器技术在工业运营中的高度渗透水平也增加了对以系统和及时的方式有效监控和管理复杂运营的方法的需求。所提出的技术通过为维护和工业工程师提供能力来满足这一需求，以实现成本高昂的制造设备故障的平均解决时间（MTTR）接近零。此外，该技术通过改进分析数据并在故障发生后提供见解的方法来消除机器故障。这个I-Corps项目基于以下开发：1）专有的可编程逻辑控制器（PLC）驱动程序，用于捕获机器组件的微退化趋势; 2）机器生态系统的数字孪生模型，反映物理系统性能和机器之间的交互;以及3）自动化智能网络。该技术独特的机器性能数据集由其PLC驱动器和传感器反馈创建，捕获机器的“正常”操作。 然后，这些数据集用于创建系统不同元素之间的因果关系图。因果图可以加速检测潜在机器故障的根本原因，并预测系统中的潜在风险和弱点。此外，数字孪生提供了一个网络物理平台，该平台集成了基于物理的机器组件模型、计算机辅助工程（CAE）模型的空间关系以及从自适应机器学习方法中推断出的数据驱动相关性。AI平台在分布式系统/边缘分析网络上运行，以确保对本地节点（例如，单独的机器及其部件）和系统（例如，生产线）水平。这三个元素的整合建立了一个可扩展的数字孪生模型，利用元素交互来不断发现和优化因果关系，从而快速可靠地检测、识别和查明问题。该奖项反映了NSF的法定使命，并通过使用基金会的知识价值和更广泛的影响审查标准进行评估，被认为值得支持。