EAGER: Electro-Mechanical Energy Conversion System Reliability through Self-Characterization and Self-Healing Approaches

Electro-mechanical energy conversion systems are an essential component of our day-to-day life, ranging from electrified transportation, to manufacturing systems and beyond. These energy conversion systems consist of an array of critical sub-systems of varying degrees of complexity that operate seamlessly and efficiently. Efficient and reliable operation of the system as a whole is governed by the efficient, optimal, and reliable operation of each subsystem. Reduced performance, partial failure, or complete failure of a single subsystem impacts the overall energy conversion process and is deemed costly, wasteful, and possibly harmful. Thus, research on diagnosing inefficiencies, failure mode detection, and fault mitigation in electromechanical energy conversion systems has existed for many decades. However, limitations in the existing fault diagnosis framework necessitate novel and robust approaches. The exploratory research aims to investigate self-optimizing extremum-seeking control (SO-ESC) approaches, which enable the system to self-heal and self-characterize without disrupting the overall energy conversion process. Discoveries made from this work will establish a new paradigm of controllers and novel approaches that allow systems to be highly reliable, aligning with NSF's mission to promote the progress of science and advance the capability of the nation to meet current and future challenges. The project intends to investigate fundamental approaches that can lead to transformative technology allowing machine-drive systems to self-heal under fault conditions and self-characterize for optimized performance. This will be achieved through a self-optimizing extremum-seeking framework capable of operating under static or dynamic states, in which the underlying sub-systems are monitored and optimized while the energy conversion system is in use. Once implemented, the energy conversion system will be able to 1) optimize performance without human intervention, 2) facilitate online fault mitigation without user/human intervention and 3) specifically localize sub-systems that require attention. The aforementioned outcomes will be achieved with extremum-seeking controls that utilize a minute perturbation to direct the non-linear system to optimal performance. Non-linear properties of fault modes and inefficiencies along with the perturbation used for extremum-seeking, will assist in achieving optimal performance and self-optimization of the extremum-seeking controller in real-time. The ability to self-optimize the extremum-seeking approach adds an additional layer of benefit, enabling the energy conversion systems to maintain optimal performance despite a range of variables changing, which influence system performance. 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.