关键词: 测地距离, 谱聚类, 生产状态分析, 聚类分析

Abstract: With the development of computer and sensor technology, a large number of process data during manufacturing is collected. Extracting the knowledge and information from the data is an important method to improve the product quality. The clustering method with process data is used to acquire the production status, thus for process diagnosis or enhancing the focal points of the quality inspect. Spectral clustering is one of the advanced methods, where the Euclidean distance is used as the common similarity measure, which can only extract the features of the spherical or hyperspherical distribution data but can not express the complex distribution data. In this paper geodesic distance is introduced to spectral clustering, which is used to do the production state clustering. The benchmark data, Tennessee Eastman process data are used for model validation. As a result the proposed method reduces the parameter sensitivity and has better performance on clustering to acquire the production state, compared with the Euclidean distance.

Key words: geodesic distance, spectral clustering, production state analysis, clustering analysis