Development of Intelligent Linear Axis Digital Twin for Health Condition Monitoring

该研究项目涉及圭尔夫大学（圭尔夫，安大略）的智能控制和估计（ICE）实验室（Gadsden博士）、加拿大福特汽车公司（温莎，安大略）和帕蒂工程公司（奥本希尔斯，密歇根州，美国）之间的合作。合作研究伙伴关系的主要目标是开发和应用新的人工智能（AI）和估计理论技术，以改善制造系统的状态监测。监控制造系统的健康状况将提高整体零件质量，减少制造停机时间，并最大限度地减少计划外维护。潜在的成果和影响包括：1）开发基于人工智能策略和新估计方法的新算法和技术; 2）开发用于提高制造系统可靠性和安全性的状态监测策略; 3）开发用于互连制造系统的新集成软件和技术; 4）将研究成果转化为公共和私营部门的可能性，这将为加拿大带来社会经济效益; 5）培养高素质人才（HQP），使其成为人工智能和制造领域未来的领导者和员工。

This research project involves a partnership among the Intelligent Control and Estimation (ICE) Laboratory (Dr. Gadsden) at the University of Guelph (Guelph, Ontario), Ford Motor Company of Canada (Windsor, Ontario), and Patti Engineering (Auburn Hills, Michigan, USA). The primary goal of the collaborative research partnership is to develop and apply novel artificial intelligence (AI) and estimation theory techniques to improve the condition monitoring of manufacturing systems. Monitoring the health of manufacturing systems will improve overall part quality, reduce manufacturing downtime, and minimize unscheduled maintenance. The potential outcomes and impacts include: 1) development of new algorithms and techniques based on AI strategies and new estimation methods; 2) development of condition monitoring strategies for improved reliability and safety of manufacturing systems; 3) development of new integration software and techniques for interconnecting manufacturing systems; 4) possibility to translate research discoveries in the public and private sectors, which will lead to socio-economic benefits for Canada; and, 5) training of High Quality Personnel (HQP) as future leaders and employees in the AI and manufacturing fields.