I-Corps: Development of decentralized anomaly detection for industrial facilities

I-Corps项目更广泛的影响/商业潜力是技术的发展，这将有助于数字控制器检测和识别工业系统中的异常。每个工业部门都依赖于工业控制系统的运作。通过数据驱动的洞察力，降低意外故障的风险，提高工人的知识和操作效率，这些技术具有重大的商业利益。现有的解决方案在网络安全、安装成本、如何使用解决方案的员工知识以及解释异常观察结果的能力等方面存在共同的问题。通过与云分离并专注于技术的自主和可解释功能，使用该技术的技术人员可能能够快速有效地完成工作，而无需成为人工智能/机器学习（AI/ML）方面的专家。采用“即插即用”的方法将减少在小型系统上部署分析的进入障碍，从而能够更好地了解设备状态，并在紧急情况发生之前避免设备退化。该技术还将使当前工业解决方案领域中代表性不足的实体受益，因为该解决方案可以降低实施成本和基础设施重新设计。这项技术可以减少工业停机时间，提高整体设施和过程的可靠性，从而降低美国制造的成本。I-Corps项目的基础是开发一种自主的、基于边缘的分析技术，用于分散和可扩展的故障检测。其核心技术是在极小的计算空间内实现工业控制系统故障自动检测的高效软件。这样的解决方案将使分析的部署成为可能，甚至对于通常在分析计划中被忽视的小规模系统也是如此。该技术基于现场数字工业资产的数据，自主收集、分析和构建分析AI模型，然后使用这些模型持续监测与设备退化或故障相关的异常行为迹象，所有这些都不需要连接到云。该软件源自基于核的回归方法，因此所有的预测和决定都可以直接内省和解释。这些功能与简单易懂的用户界面相匹配，可以交互式地用于识别驱动给定报警系统的根信号。该奖项反映了美国国家科学基金会的法定使命，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。

The broader impact/commercial potential of this I-Corps project is the development of technology that will aid digital controllers in detecting and identifying anomalies in industrial systems. Every industrial sector relies on industrial control systems to function. There is significant commercial interest in technologies which reduce the risk of unplanned failure and improve worker knowledge and operational effectiveness through data-driven insights. Existing solutions share common issues around cybersecurity, cost of installation, workforce knowledge on how to use the solution, and the ability to explain unusual observations. By separating from the cloud and focusing on the autonomous and explainable capabilities of the technology, technicians using this technology may be able to perform their jobs rapidly and more effectively without needing be experts in artificial intelligence / machine learning (AI/ML) themselves. Adopting a “plug-and-play” approach will reduce the barrier to entry for deploying analytics on smaller systems, thereby enabling better awareness of facility states and avoiding facility degradation before it becomes urgent. The technology will also benefit underrepresented entities in the current industrial solutions landscape as the solution may reduce the cost of implementation and the infrastructure redesigns. This technology may reduce industrial downtime and improve overall facility and process reliability, thereby reducing costs of US-based manufacturing.This I-Corps project is based on the development of an autonomous, edge-based analytics technology for decentralized and scalable fault detection. The core technology is an efficient software which can detect faults in an industrial control system autonomously using on a very small computational footprint. Such a solution will make the deployment of analytics possible even for small-scale systems which are normally overlooked in analytics initiatives. The proposed technology autonomously collects, analyzes and builds analytical AI models based on data from digital industrial assets in the field and then uses these models to continuously monitor for signs of anomalous behavior corresponding to equipment degradation or malfunction, all with no connection to the cloud. The software is derived from kernel-based regression methods, such that all predictions and decisions can be directly introspected and explained. These capabilities are paired with a simple and easy-to-understand user interface which can be interactively used to identify the root signals driving a given alarm system.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.