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被用于工业培训和几个行业的预测性维护，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。需要填补的职位与能够以欧元计价填补这些职位的技能库之间也存在很大差距。此外，老龄化的劳动力正在造成拥有现场专业知识的工人流失的风险。在电力系统中维护用于发电、储存、传输和分配的现代设备既昂贵又困难，因为这些设备用途更多，本质上也更复杂。因此，对它们的有效和富有成效的维护提出了挑战。该项目旨在设计一个框架，通过采用新兴技术来满足欧洲联盟机构的智能培训和预测性维护的需要，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，改进后的培训将缩小熟练工人和非熟练工人之间的技能差距，并提高工作场所的情景意识和安全。预测性维护模型将通过预测维护需求和设备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员工生产力，提高工作效率，并降低计划外维护的成本。将应用最先进的ML方法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，最终将节省时间和人力，并提高客户满意度。将采用综合措施和衡量标准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，以了解人工智能和XR技术如何改变EUI中的工作和劳动力。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传播。该奖项反映了NSF的法定使命，并通过使用基金会的智力优势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.