Fault-tolerant mining algorithm of sampling data from dynamic system
Fault-tolerant mining algorithm of sampling data from dynamic system
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DOI：
10.1109/ccdc.2013.6561826
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发表时间：
2013-05
期刊：
影响因子：
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通讯作者：
Hu Shaolin;Li Ye;Zhang Dong
中科院分区：
文献类型：
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作者：
Hu Shaolin;Li Ye;Zhang Dong
时间序列数据挖掘是我们设计数据驱动的状态监测和故障诊断系统的有用工具。针对动态过程的异常变化监测，建立了一系列挖掘算法来挖掘采样数据序列中的信号形式、过程模型结构和噪声统计特性，建立了采样时间序列信息挖掘系统的体系结构。这些算法对于复杂系统采样数据集中的零散异常值具有很强的容错能力。本文给出的结果不仅可用于复杂动态过程的安全分析和故障诊断，还可用于变化检测等相关领域。
Time series data mining is an useful tool for us to design data-driven condition monitoring as well as fault diagnosis system. Aiming at monitoring abnormal changes of dynamic process, a series of mining algorithms are built up to mine signal form, model structure of process and statistical properties of noise in sampling data series, the architecture of information mining system of sampling time series is set up. These algorithms are very fault tolerant for patchy outliers in sampling data set of the complex system. Results given in this paper can be used not only in the safety analysis and fault diagnosis of complicated dynamic process but also in change detection as well as other related fields.