Real-Time Deep Learning at the Edge for Scalable Reliability Modeling of Si-MOSFET Power Electronics Converters

Abstract

With the significant growth of advanced high-frequency power converters, online monitoring and active reliability assessment of power electronic devices are extremely crucial. This paper presents a transformative approach, named deep learning reliability awareness of converters at the edge (Deep RACE), for real-time reliability modeling and prediction of high-frequency MOSFET power electronic converters. Deep RACE offers a holistic solution which comprises algorithm advances, and full system integration (from the cloud down to the edge node) to create a near real-time reliability awareness. On the algorithm side, this paper proposes a deep learning algorithmic solution based on stacked long short-term memory for collective reliability training and inference across collective MOSFET converters based on device resistance changes. Deep RACE also proposes an integrative edge-to-cloud solution to offer a scalable decentralized devices-specific reliability monitoring, awareness, and modeling. The MOSFET convertors are Internet-of-Things (IoT) devices which have been empowered with edge real-time deep learning processing capabilities. The proposed Deep RACE solution has been prototyped and implemented through learning from MOSFET data set provided by NASA. Our experimental results show an average miss prediction of 8.9% over five different devices which is a much higher accuracy compared to well-known classical approaches (Kalman filter and particle filter). Deep RACE only requires 26-ms processing time and 1.87-W computing power on edge IoT device.