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

合作研究：FW-HTF-P：IntelEUI：人工智能和扩展现实可提高能源和公用事业行业的劳动力生产力

• 批准号: 2129093
• 负责人: Sidike Paheding
• 金额: $ 6.97万
• 依托单位: Michigan Technological 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: 2021-09-15 至 2023-08-31
• 项目状态: 已结题
• 来源: https://www.nsf.gov/awardsearch/showAward?AWD_ID=2129093&HistoricalAwards=false
• 关键词: Collaborative; Research; FW; HTF; IntelEUI

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.

新兴计算技术最近已应用于多个行业的工业培训和预测性维护，以提高员工生产力并提高制造和产量。然而，能源和公用事业行业（EUI）对技术的采用有限。 EUI 中待填补的职位与能够填补这些职位的技术人才库之间也存在巨大差距。此外，劳动力老龄化还带来了失去拥有现场实践专业知识的工人的风险。维护 EUI 中的现代发电、存储、输电和配电设备既昂贵又艰巨，因为它们用途更广泛且本质上更复杂。因此，对其高效且高效的维护提出了挑战。该项目旨在通过采用新兴技术设计一个框架，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。项目研究人员与 EUI 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，并提高工作场所的态势感知和安全性。预测性维护模型将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人工智能（AI）、机器学习（ML）和扩展现实（XR）等尖端技术，以改善通过可定制的有效培训提高员工生产力，提高工作效率，并降低计划外维护成本。最先进的机器学习方法将用于开发该框架的预测维护模块，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、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。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 EUI 中的工作和劳动力。该项目的研究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价值和更广泛的影响审查标准进行评估，被认为值得支持。

项目成果

Augmented Reality and Artificial Intelligence in industry: Trends, tools, and future challenges

工业中的增强现实和人工智能：趋势、工具和未来的挑战

• DOI: 10.1016/j.eswa.2022.118002
• 发表时间: 2022-11
• 期刊: Expert Systems with Applications
• 影响因子: 8.5
• 作者: Devagiri, Jeevan S.;Paheding, Sidike;Niyaz, Quamar;Yang, Xiaoli;Smith, Samantha
• 通讯作者: Smith, Samantha

Deep-Learning-Incorporated Augmented Reality Application for Engineering Lab Training

用于工程实验室培训的深度学习增强现实应用

• DOI: 10.3390/app12105159
• 发表时间: 2022-05
• 期刊: Applied Sciences
• 作者: Estrada, John;Paheding, Sidike;Yang, Xiaoli;Niyaz, Quamar
• 通讯作者: Niyaz, Quamar