期刊
IEEE TRANSACTIONS ON COMPUTER-AIDED DESIGN OF INTEGRATED CIRCUITS AND SYSTEMS
卷 33, 期 2, 页码 279-290出版社
IEEE-INST ELECTRICAL ELECTRONICS ENGINEERS INC
DOI: 10.1109/TCAD.2013.2287184
关键词
Board-level fault diagnosis; functional failures; incremental learning; kernel; machine learning; support-vector machines
类别
资金
- Huawei Technologies
Advanced machine learning techniques offer an unprecedented opportunity to increase the accuracy of board-level functional fault diagnosis and reduce product cost through successful repair. Ambiguous or incorrect diagnosis results lead to long debug times and even wrong repair actions, which significantly increase repair cost. We propose a smart diagnosis method based on multikernel support vector machines (MK-SVMs) and incremental learning. The MK-SVM method leverages a linear combination of single kernels to achieve accurate faulty-component classification based on the errors observed. The MK-SVMs thus generated can also be updated based on incremental learning, which allows the diagnosis system to quickly adapt to new error observations and provide even more accurate fault diagnosis. Two complex boards from industry, currently in volume production, are used to validate the proposed diagnosis approach in terms of diagnosis accuracy (success rate) and quantifiable improvements over previously proposed machine-learning methods based on several single-kernel SVMs and artificial neural networks.
作者
我是这篇论文的作者
点击您的名字以认领此论文并将其添加到您的个人资料中。
推荐
暂无数据