arXiv 1908.012442019-08-03cs.LGeess.SP

边缘实时的深度学习用于Si-MOSFET电力电子转换器的可扩展可靠性建模

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

The University of North Carolina at Charlotte（北卡罗来纳大学夏洛特分校）

Mohammadreza Baharani, Mehrdad Biglarbegian, Babak Parkhideh, Hamed Tabkhi

备注：: 2019 IEEE. Personal use of this material is permitted. Permission from IEEE must be obtained for all other uses, in any current or future media, including reprinting/republishing this material for advertising or promotional purposes, creating new collective works, for resale or redistribution to servers or lists, or reuse of any copyrighted component of this work in other works
发表信息：: IEEE Internet of Things Journal, 2019
DOI：: 10.1109/JIOT.2019.2896174

摘要：

随着先进高频功率变换器的显著增长，电力电子器件的在线监测和主动可靠性评估是极其重要的。本文提出了一种变革性的方法，称为边缘转换器的深度学习可靠性意识（Deep RACE），用于高频MOSFET电力电子转换器的实时的【形】可靠性建模和预测。Deep RACE提供了一个整体解决方案，包括算法进步和完整的系统集成（从云端到边缘节点），以创建近乎实时的【形】的可靠性感知。在算法方面，本文提出了一种基于堆叠LSTM的深度学习算法解决方案，用于基于器件电阻变化的集体MOSFET转换器的集体可靠性训练和推理。Deep RACE还提出了一种集成的边缘到云解决方案，以提供可扩展的分散式设备特定可靠性监控，感知和建模。MOSFET转换器是具有边缘实时的【形】深度学习处理能力的物联网设备。提出的Deep RACE解决方案已通过学习NASA提供的MOSFET数据集进行了原型设计和实现。我们的实验结果表明，平均错过预测$8.9\%$在五个不同的设备，这是一个更高的精度相比，众所周知的经典方法（卡尔曼滤波器，粒子滤波器）。Deep RACE仅需要边缘物联网设备上的$26ms$处理时间和$1.87W$计算能力。

英文摘要：

With the significant growth of advanced high-frequency power converters, on-line monitoring and active reliability assessment of power electronic devices are extremely crucial. This article 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 LSTM 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 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 $26ms$ processing time and $1.87W$ computing power on Edge IoT device.

机构、总结、中文摘要均由AI分析而来，可能存在错误，请以论文正文为准。