Abstract

In classical control engineering, optimality and robustness have been the main concerns of the control design and maintaining good performance. The third main concern can be considered as smartness with the inevitable grow of Digital Transformation and Industry 4.0. The core technologies enable users to increase capabilities of the systems not only for the design but also for maintaining a successful operation afterwards. Many engineering applications require a proper maintenance strategy to address the degradation and failure in the machines, processes and complex systems. Predictive maintenance strategy enables users to find optimal time and part selection to reduce downtime and maximize equipment lifetime. With the introduction of smartness to the predictive maintenance, a new frontier of Smart Predictive Maintenance (SPM) is aimed in this thesis to address main obstacles of traditional predictive maintenance workflow with key enabling technologies of Digital Twins (DT) and physics-informed Smart Big Data (SBD). To enhance the framework, development of the Digital Twin with behavioral matching process and utilization of existing knowledge in the Smart Big Data are demonstrated. A set of case studies including physics-informed transfer learning for fault classification, smart selection of control elements and error recovery for the Radio Frequency Impedance Matching (RFIM) system are presented. Results show that SPM is a new and effective systematic approach that can improve maintenance strategies, health monitoring and fault diagnosis applications.

Biography

Furkan Guc is a PhD candidate in Mechanical Engineering at University of California, Merced. He is working in Mechatronics, Embedded Systems and Automation (MESA) Lab, and advised by Prof. YangQuan Chen with research interests of applied fractional calculus, smart control engineering, industrial AI, control performance assessment, fault diagnosis and health monitoring in process control. Before coming to UC Merced, he received BS and MSc degrees in the Department of Mechanical Engineering at Bilkent University, where he had a chance to work in Bilkent Miniature Robotics Lab (MRL) with a focus on miniature robotics, nonlinear system identification and control. He is a passionate scholar and dilettante, who enjoys basketball and board games.