计算机工程2017，Vol.43Issue(5)：156-162,7.DOI:10.3969/j.issn.1000-3428.2017.05.025
基于不平衡支持向量数据描述的故障诊断算法
Fault Diagnosis Algorithm Based on Imbalanced Support Vector Data Description
摘要
Abstract
This paper analyzes the characteristics of the unsupervised and supervised fault diagnosis methods,and presents an Imbalanced Support Vector Data Description (ISVDD) algorithm,which combines both advantages.The algorithm can find the most representative feature based on the description of the boundary distribution of samples under normal operating conditions.It takes in the supervised fault diagnosis method,and describes the true boundary of samples under normal operating conditions more correctly by introducing the discriminant information from fault operating conditions.It is optimized for fault detection where the imbalance data is common.The empirical error which is sensitive to the sample distribution in traditional SVDD is replaced by the Area Under Curve(AUC) which is robust to the sample distribution.Numerical simulation and industrial cases are presented to verify the effectiveness of the proposed algorithm.关键词
故障诊断/数据驱动/支持向量数据描述/不平衡数据/SECOM数据集Key words
fault diagnosis/data driven/Support Vector Data Description (SVDD)/imbalanced data/SECOM dataset分类
信息技术与安全科学引用本文复制引用
韩志艳,王健..基于不平衡支持向量数据描述的故障诊断算法[J].计算机工程,2017,43(5):156-162,7.基金项目
国家自然科学基金(61503038,61403042). （61503038,61403042）
