Industrial process data clustering method for density peak clustering

Abstract

The invention discloses an industrial process data clustering method for density peak clustering, which comprises: acquiring industrial process data to form a data set; combining the Euclidean distance between data in the data set with a time factor to obtain the distance between the data; obtaining a truncation distance according to the distance between the data and an adjustment parameter, so asto obtain the local density of each data, and calculating the minimum distances between each data and the data having local density larger than its local density; ordering the products of the local density of each data in the data set and the minimum distances, using the first H pieces of data with large products as clustering centers, and determining that the data, in the data with larger localdensity than the cluster center, closest to the clustering center and the clustering center belong to the same category; determining the class attribute of the data without class attributes in the data set according to a descending order of the local densities, so as to obtain the clustering result of industrial process data. The clustering centers of the invention are reasonable, and the number of clustering centers is automatically determined and less time complexity is achieved.

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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