Process fault identification method based on big data intelligent kernel independent component analysis

Abstract

The invention provides a process fault identification method based on a big data intelligent kernel independent component analysis, and relates to the technical field of fault diagnosis in the processindustry. The method includes: constructing a semi-supervised kernel independent component analysis algorithm through sample data, obtaining a spatial conversion matrix and a state projection matrixof the sample data, and constructing a production operation state library of each operation state category; and preprocessing newly acquired data, performing preliminary fault diagnosis through the obtained spatial conversion matrix and the state projection matrix, calculating a scoring factor of real-time condition data through an obtained confidence interval in each projection direction, calculating a FICD statistic, and performing accurate fault identification. According to the method, with the combination of a semi-supervised classification learning method based on class membership and thekernel independent component analysis, fault diagnosis and accurate fault identification of the operation state of an industrial process are performed according to the state projection matrix and theconstruction of the corresponding statistic, and the identification degree and the accuracy for identifying the smelting operation state of an electrical smelting furnace for magnesia can be effectively improved.

Description

technical field [0001] The invention relates to the technical field of process industry fault diagnosis, in particular to a process fault identification method based on big data intelligent core independent element analysis. Background technique [0002] Traditional datasets often consist of process variables such as physical chemistry. Vector-based process monitoring methods have flourished under the impetus of the use of multivariate statistical methods. However, after the extensive use of monitoring equipment, the emergence of heterogeneous matrix data such as images and videos with high dimensions, rich data volume, and obvious dynamic performance has made the field of fault diagnosis encounter challenges in new application prospects. [0003] Feature extraction and feature selection are of great help to the above problems. However, it is difficult to mark the state information of each data with too rich and massive data sources. Therefore, the modeling process of faul...

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Problems solved by technology

During the smelting process, due to the imbalance of the current or the slight deviation of the electrode position, the abnormal emission of carbon dioxi

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