Collaborative Research: FW-HTF-P: IntelEUI: Artificial Intelligence and Extended Reality to Enhance Workforce Productivity for the Energy and Utilities Industry

新兴的计算技术最近已被用于多个行业的工业培训和预测性维护，以提高劳动力生产率并增加制造和生产。然而，能源和公用事业行业（EUI）的技术采用有限。在需要填补的职位和能够填补这些职位的技术人才库之间也存在很大差距。此外，老龄化的劳动力正在造成失去具有现场专业知识的工人的风险。在EUI中维护用于发电、存储、传输和分配的现代设备是昂贵且艰巨的，因为它们更加通用且固有地复杂。因此，对其进行高效且富有成效的维护面临挑战。该项目旨在设计一个框架，通过采用新兴技术来满足EUI中智能培训和预测性维护的需求，并开发该框架的工作原型。项目调查人员与欧盟机构合作设计框架。从长远来看，改进的培训将缩小熟练工人和低技能工人之间的技能差距，并提高对工作场所情况的认识和安全。该项目在框架设计和开发中集成了人工智能（AI）、机器学习（ML）和延展实境（XR）等前沿技术，通过可定制和有效的培训提高员工生产力，提高工作效率，降低计划外维护成本。最先进的机器学习方法将用于开发框架的预测性维护模块，以提高EUI中各种设备的可靠性和可持续性，最终节省时间，人力，并提高客户满意度。将采用全面的措施和指标来评估该框架在行业背景下的技术，经济和社会影响。提出了一组研究问题，以了解AI和XR技术如何改变EUI中的工作和劳动力。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。

Emerging computing technologies have been recently employed for industrial training and predictive maintenance in several industries to improve workforce productivity and increase manufacturing and production. However, there is limited adoption of technologies in Energy and Utilities Industries (EUIs). There is also a wide gap between the jobs to be filled and the skilled pool capable of filling them in EUIs. Additionally, the aging workforce is creating a risk of losing workers with hands-on field expertise. Maintaining contemporary equipment for power generation, storage, transmission, and distribution in EUIs is expensive and arduous as they are more versatile and inherently complicated. Therefore, challenges arise for their efficient and productive maintenance. The project aims to design a framework that will meet the needs of smart training and predictive maintenance in EUIs by employing emerging technologies and develop a working prototype of the framework. The project investigators collaborate with EUIs to design the framework. In the long-term, the improved training will reduce the skill gap between skilled and less-skilled workers and increase situational awareness and safety in the workplace. The predictive maintenance model will reduce costs by predicting maintenance needs and downtime of equipment.The project integrates cutting-edge technologies in the framework design and development including Artificial Intelligence (AI), Machine Learning (ML), and Extended Reality (XR) to improve workforce productivity through customizable and effective training, enhance work efficiency, and reduce cost on unplanned maintenance. State-of-the-art ML methods will be applied to develop the predictive maintenance module of the framework to improve reliability and sustainability of various equipment in EUIs that will eventually save time, human efforts, and increase customer satisfaction. Comprehensive measures and metrics will be employed to assess the technology, economic, and social impact of the framework in the industry context. A set of research questions is proposed to understand how AI and XR technology is transforming work and workforce in EUIs. The project findings will be disseminated to the academic and industry community through a dedicated website, research publications, and social media platforms.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.