Improved-principle-component-tracking-based industrial process monitoring method and application

Abstract

The invention, which belongs to the technical field of the industrial process monitoring and diagnosis, discloses an improved-principle-component-tracking-based industrial process monitoring method and application. Decomposition of a low-rank-matrix-expression-based principle component tracking method is carried out on industrial collection data to obtain a low-rank coefficient matrix including all variable relations of the process; and on the basis of the low-rank coefficient matrix as well as correlated coefficient weights of variables in a training matrix, an L2 statistic is constructed to carry out fault detection and identification. According to the invention, according to the principles of the low-rank matrix expression and the principle component tracking method, the low-rank matrix expression algorithm is fused into the principle component tracking to construct a low-rank-matrix-expression-based principle component tracking algorithm model; and the model is used for carrying out on-line monitoring. Therefore, the correlated relations between variables in the training matrix and effective information included by the variables in the training matrix are utilized fully. With the method, the accuracy of industrial fault detection and identification with abnormal values is high.

Description

technical field [0001] The invention belongs to the field of industrial process monitoring and fault diagnosis, in particular to an online industrial fault detection and identification based on principal component tracking represented by a low-rank matrix, using L 2 Statistics. Background technique [0002] Industrial process production is the pillar industry of national economic development, so it is very important to ensure the efficiency and stability of the production process. Process monitoring is mainly divided into four steps: model building, fault detection, fault identification, and process reconstruction. Fault detection and identification is a critical step in process monitoring. [0003] Industrial process monitoring methods are divided into three categories: methods based on quantitative mathematical models, methods based on knowledge and methods based on data-driven. Compared with mechanism-based models and knowledge-based methods, data-driven process monito...

AI Technical Summary

Problems solved by technology

This method applies the principal component tracking method and uses the PCA statistics in the low-rank matrix for fault detection, but as mentioned above, the statistics of the PCA method are not suitable for direct application in the principal component tracking method

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