中文摘要：

为发现生产过程中的故障传递和相互影响规则,并用于故障诊断,在监测系统的时序数据分析中引入序列挖掘技术,提出了采用基于核密度估计的符号空间划分方法,利用数据本身的分布特性对连续数值形的时序数据进行符号化,得到适于挖掘的符号序列。通过故障时窗约束、序列集成和序列化简,将多维非同步时间序列转化为与故障相关的序列数据库。在此基础上,采用序列模式挖掘算法对Tennessee-Eastman仿真数据进行序列挖掘,得到了以时序模式表示的故障过程的主要变化信息。实验表明该方法是可行且有效的。

英文摘要：

To find the rules of fault transfer and their mutual influences, sequential pattern mining technology was introduced into analysis of time series in monitoring system. A symbolizing method based on kernel density estimation was proposed. Characteristics of data distribution were used to transform multiple time series of consecutive numerical values into one symbol series so as to obtain suitable mining sequential pattern. After the data restriction, integration, and simplification, a sequence data set which was relevant to faults was formed. A sequential pattern mining algorithm was adopted to process the synthetic data of Tennessee-Eastman procedure. The resulting sequential patterns indicated the main change information of the production of fault. Test demonstrated the feasibility and validity of this method. It helped engineers to understand inherent interactional relationships in complex system in order to make reasonable diagnosis.