基于Transformer的DC/DC板级验证状态识别  

DC/DC board-level verification status recognition based on Transformer

作　　者：于海波 李杰[1,2] 胡陈君[1,2] 夏俊辉 张伟 YU Haibo;LI Jie;HU Chenjun;XIA Junhui;ZHANG Wei(Key Laboratory of Electronic Testing Technology for National Defense Science and Technology,North University of China,Taiyuan 030051,China;Key Laboratory of Instrument Science and Dynamic Measurement,Ministry of Education,North University of China,Taiyuan 030051,China;Huaihai Industrial Group Co.,Ltd.,Changzhi 046012,China)

机构地区：[1]中北大学动态测试技术省部共建国家重点实验室,太原030051 [2]中北大学仪器科学与动态测试教育部重点实验室,太原030051 [3]淮海工业集团有限公司,长治046012

出　　处：《集成电路与嵌入式系统》2024年第5期94-100,共7页INTEGRATED CIRCUITS AND EMBEDDED SYSTEMS

基　　金：高动态短航时弹用半捷联惯性测量系统误差自补偿理论与方法研究(61973280);弹载组合导航射后空中一体化对准理论与方法研究(2021030212241864)。

摘　　要：为满足航天产品的高精度、高可靠性需求,实现元器件自主可控、芯片国产化及应用适应性验证十分必要,设计一种基于FPGA的国产DC/DC板级综合测试平台。在长时间的热学环境适应性板级验证项目中,为实现DC/DC器件应用板卡工作状态的实时监测,提出一种基于Transformer的智能识别算法。分别使用空载、负载电流3 A、负载电流5 A、高输入电压、低输入电压、短路状态下的DC DC输出序列,输入到Transformer模型中并利用注意力机制提取各序列的全局注意力特征,并对深度学习模型进行训练。实验结果表明,对于此6种工作状态数据集,Transformer模型识别的准确率为99.2%,具备良好的分类和监测性能,具有一定的工程应用价值。To meet the high-precision and high-reliability requirements of aerospace products,and achieve self-reliance in components and chip localization,as well as application adaptability verification,it is necessary to design a domestic DC/DC board-level comprehensive testing platform based on FPGA.In long-term thermal environmental adaptability board-level verification projects,to achieve real-time monitoring of the working status of DC/DC device application boards,a smart recognition algorithm based on Transformer is proposed.The deep learning model is trained using these features.The experiment results show that for this 6-state dataset,the recognition accuracy of the Transformer model is 99.2%,which has good classification and monitoring performance and has certain engineering application value.

关 键 词：FPGA 板级测试 状态识别 深度学习 Transformer模型 

分 类 号：TP391.4[自动化与计算机技术—计算机应用技术]