MOSFET Junction Temperature Measurements using Conducted Electromagnetic Emissions and Support Vector Machines

As power electronics permeate critical infrastructure in modern society, more precise and effective diagnostic methods are required to improve system reliability as well as reduce maintenance costs and unexpected failures. Prognostic and Health Management (PHM) systems that analyze changes in the electromagnetic spectrum (E-PHM) of a circuit can be implemented to determine the health of the equipment under test. This research demonstrates the use of E-PHM techniques to measure the junction temperature of a silicon carbide (SiC) MOSFET. The results show the feasibility of training machine learning algorithms to recognize this relationship and determine the junction temperature within 10 °C. This is accomplished, in situ, without interruption of device operation and without altering the system's performance.