关键词: 熵值评估, 谱聚类, 生产状态分析, 聚类分析

Abstract: The real production process data are always multi-model. The clustering method with process data is used to acquire the production status. The traditional linear methods can not extract the nonlinear relationship among the data. Spectral clustering is one of the advanced methods, where the eigenvector is selected based on the eigenvalue. The eigenvalue only can express the variation information of the data. In real field, there are the complex distribution data. The entropy ranking is introduced to spectral clustering, which is used to do the production state clustering. The benchmark data, Tennessee Eastman（TE） process data are used for model validation, as a result the proposed method has better performance on clustering to acquire the production state, compared with the classical spectral clustering.

Key words: entropy ranking, spectral clustering, production state analysis, clustering analysis