【作者】 周福娜； 文成林； 陈志国；

【Author】 Zhou Funa1, Wen Chenglin2,Chen Zhiguo 1. Computer & Information Engineering School,Henan University, Kaifeng, 475004, China2.Institute of System Modeling & Control,Hangdian University;Hangzhou,310018, China

【机构】 河南大学计算机与信息工程学院； 杭州电子科技大学系统建模与控制研究所；

【摘要】 多变量统计异常检测方法的理论基础是假设检验,要求所采集到的观测数据是同一随机变量的样本实现。受环境、系统负荷变化等因素的影响,这一条件通常无法满足。本文提出一种基于扩展多尺度主元分析的多模型异常检测方法,通过在线数据驱动的方式自适应选取历史数据在不同频率上的信息,建立更准确的统计模型,从而可以较好地检测系统在不同时段内运行时可能会发生的多种频率类型故障。仿真分析表明了本文方法的有效性。更多还原

【Abstract】 Theory foundation of multivariate statistical anomaly detection is hypothesis test. which require that all data to establish the statistical model must be samples of a unique set of random variables. In most cases, due to the affect of environment and load variation of the system; this assumption can’t be satisfied. In addition; during different operation substage of a system, faults with different frequency may be occurred. This will decrease the anomaly detection efficiency. In this paper, we propose a multi-model anomaly detection method, which can adaptively select history data on different scale to establish the statistical anomaly detection model. Thus we can use the multi-model to well detect every possible fault. Simulation shows its efficiency of this algorithm.更多还原