A Clustering Method for Industrial Process Data Based on Density Peak Clustering

Abstract

The invention discloses an industrial process data clustering method of density peak clustering, comprising: obtaining industrial process data to form a data set; combining the Euclidean distance between the data in the data set with a time factor to obtain the distance between the data ;According to the distance between the data and the adjustment parameters, the cut-off distance is obtained, and then the local density of each data is obtained, and the minimum distance between each data and the data larger than its local density is calculated; for the local of each data in the data set The product of the density and the minimum distance is sorted, and the first H data with a larger product is taken as the cluster center, and the data closest to the cluster center among the data with a higher local density than the cluster center belongs to the same class as the cluster center; for the data The clustered data without class attributes judges their class attributes according to the order of local density from large to small, and then obtains the clustering results of industrial process data. The cluster center of the present invention is more reasonable, automatically determines the number of cluster centers and has less time complexity.

Description

technical field [0001] The invention belongs to the technical field of normal data, fault data and different mode classification of industrial processes, and more specifically relates to a clustering method for industrial process data of density peak clustering. Background technique [0002] For a large-scale industrial system, due to the change of production strategy and production environment, the industrial process often presents the characteristics of multi-mode and multi-fault. For different modes, we need to establish different sub-models, so that the whole model can have better performance to monitor industrial processes and predict quality indicators. Therefore, it is of great significance to conduct mode identification and fault classification for multi-mode industrial processes before modeling. [0003] Currently the most widely used methods for modal identification and fault classification are based on data-driven methods. There are two main methods, one is to u...

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Problems solved by technology

[0007] Aiming at the above defects or improvement needs of the prior art, the present invention provides an industrial process data clustering method for density peak clustering, thereby solving the problem of low accuracy of clustering results and inability to determine the best clustering center in the prior art , unable to automatically determine the number of cluster centers, technical problems with high time complexity

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