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）对技术的采用有限。欧盟需要填补的职位与能够填补这些职位的技能储备之间也存在很大差距。此外，老龄化的劳动力也带来了失去具有实际操作经验的工人的风险。维护eui中用于发电、存储、传输和配电的现代设备既昂贵又费力，因为它们更加通用，而且本身就很复杂。因此，对它们的高效和生产维护提出了挑战。该项目旨在通过采用新兴技术设计一个框架，以满足欧盟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成员合作设计框架。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，提高工作场所的态势感知和安全。预测性维护模型通过预测设备的维护需求和停机时间来降低成本。该项目在框架设计和开发中集成了包括人工智能（AI）、机器学习（ML）和扩展现实（XR）在内的尖端技术，通过可定制和有效的培训提高劳动力生产率，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应用于开发框架的预测性维护模块，以提高eu中各种设备的可靠性和可持续性，最终将节省时间、人力并提高客户满意度。将采用综合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。本文提出了一系列研究问题，以了解人工智能和XR技术如何改变欧盟的工作和劳动力。项目结果将通过专门的网站、研究出版物和社交媒体平台向学术界和工业界发布。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。

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.