Continuous chemical process fault detection method

Abstract

The invention relates to a continuous chemical process fault detection method. The continuous chemical process fault detection method comprises the following steps that (1) a linear regression model of a vector Xj and a vector Y is built, and a regression constraint function is introduced; (2) data compression is carried out through haar wavelet transformation to improve computational efficiency; (3) a regression constraint construction sparse pivot element model with the addition of 1-norm and 2-norm is built, and an optimal solution of a sparse pivot element is worked out through derivation of the SPCA algorithm; (4) the optimal threshold value of the T2 statistic and the optimal threshold value of the SPE statistic are estimated through the kernel density estimation (kde) method; (5) calculation of the T2 statistic and the SPE statistic is conducted on fault data, and the value of the T2 statistic and the value of the SPE statistic of the fault data are obtained in sequence; (6) whether a fault exists in the data is detected. According to the continuous chemical process fault detection method, the data size related to a pivot element after sparsity is reduced, so that the calculated quantity is reduced, the computation time is shortened, real-time performance of monitoring is improved, and accuracy and efficiency of fault detection can be improved.

Description

technical field [0001] The invention relates to the field of intelligent information processing, in particular to a fault detection method for continuous chemical process. Background technique [0002] With the development of control science and the advancement of industrial technology, the production capacity and modernization level of industrial systems are increasing day by day, and the production technology, production equipment and production process are becoming more and more complicated. At the same time, the potential possibility of failure of these complex systems is correspondingly increased, and the severity of the failure will also increase significantly with the complexity of the system. Once a failure occurs, it will cause great loss of personnel and property. So how to monitor the production process in real time and detect faults in advance is particularly important. Only effective process monitoring can ensure production safety and improve product quality. ...

AI Technical Summary

Problems solved by technology

[0005] The purpose of the present invention is to address the deficiencies in the existing methods, and propose a continuous chemical process fault detection method. By introducing the idea of ​​sparse pivot, the traditional dimensionality reduction problem is transformed into a regression optimization problem, so as to improve detection accuracy and detection efficiency

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