摘要： 分析无监督和监督故障诊断方法的特点,提出一种能够结合两者优势的不平衡支持向量数据描述 (ISVDD)算法。该算法具有无监督故障诊断方法的优势,通过描述正常工况样本的边界分布状况,寻找最能代表正常工况特点的特征。借鉴监督故障诊断方法,引入故障工况样本中蕴含的判别信息,更准确地描述正常工况样本的真实边界。针对故障诊断中常见的类别不平衡情况进行优化,将传统的SVDD中对样本类别分布敏感的经验误差替换为对样本类别分布鲁棒的曲线下面积。数值仿真和工业实例验证了提出算法的有效性。

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

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.

Key words: fault diagnosis, data driven, Support Vector Data Description(SVDD), imbalanced data, SECOM dataset

中图分类号: 

• TP301.6