Machine learning- assisted remaining useful lifetime prediction of power electronic converters
This paper presents a methodology for the remaining useful life (RUL) prediction in power electronic converters based on the health monitoring of semiconductor devices and DC capacitor(s). The primary components considered are Gallium Nitride High Electron Mobility Transistors (GaN HEMTs) and aluminum electrolytic capacitors (AECs). The proposed methodology leverages long-term component characterization under accelerated aging testing in a laboratory setup. A statistical appr
Researchers have developed a new method to predict the remaining operational life of power electronic converters, focusing on the health of key components like Gallium Nitride transistors and aluminum electrolytic capacitors. This approach uses data from accelerated aging tests and a statistical model to estimate the probability of survival under specific operating conditions. To simplify complex calculations, a machine learning model, specifically a neural network, processes the degradation data and statistical information. This results in a compact model deployable on microcontrollers or FPGAs for real-time monitoring within the converter itself.
This advancement allows for in-situ identification of aging or failing converters, potentially extending their operational lifespan and preventing premature replacement.
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