中文摘要：

利用区间二型模糊C-均值聚类的方法，将过程数据进行聚类，并且聚类过程采用自适应的方法选择聚类数，由此区别不同的工况；利用局部切空间排列算法（LTSA）分别对聚类之后的每一类数据进行降维处理，然后利用每一类降维后的数据，使用支持向量数据描述（SVDD）的方法构建多个模型，并建立相应的统计量与统计限，完成离线建模过程。在线监控过程中首先判断过程数据属于哪一种工况，然后利用相应的模型来计算统计量并判断是否故障，利用乙烯裂解炉的过程数据进行了仿真研究，验证了方法的可行性。

英文摘要：

This paper presents an approach which uses the interval type-2 fuzzy C-means cluster to classify the process data, and then, adaptively selects the cluster number, so that different models can be distinguished. The local tangent space alignment algorithm （LTSA） is adopted to reduce the dimensions of the data of each cluster, which is further utilized to build multi-model via the support vector data description algorithm （SVDD）, and obtain corresponding statistical magnitude and statistical limit. Thus, the offline modeling is achieved. During the online monitoring, the first is to judge which model the process data belong to, and then, to judge whether it is a fault data by calculating its statistical magnitude. Finally, the simulation on the process data of ethylene cracking furnace is made to verify the feasibility of the proposed approach in this work.