Aluminum electrolysis cell condition diagnosing method based on sub-feature space optimization relative matrix

Abstract

The invention discloses an aluminum electrolysis cell condition diagnosing method based on a sub-feature space optimization relative matrix. The aluminum electrolysis cell condition diagnosing method based on the sub-feature space optimization relative matrix is characterized by including that 1, gathering an original measurement sample set, pre-processing the original measurement sample set, and projecting to a kernel space; 2, analyzing relative principal components of a centralization matrix, building an aluminum electrolysis cell condition diagnosing model, and diagnosing the aluminum electrolysis cell condition; 3, finding out the optimal relative transformation matrix in a search region through a bacterial foraging algorithm; 4, using the optimal relative transformation matrix to build the aluminum electrolysis cell condition diagnosing model according to the step 2 so as to precisely diagnose the aluminum electrolysis cell condition. The aluminum electrolysis cell condition diagnosing method based on the sub-feature space optimization relative matrix takes full account of the nonlinear feature of the aluminum electrolysis cell condition, nonlinear parameters are projected to a high-dimensional linear feature space through kernel functions, the relative transformation matrix is optimized in the kernel space by the aid of the bacterial foraging algorithm, and the aluminum electrolysis cell fault diagnosing precision is greatly improved through the relative principal component analysis.

Description

technical field [0001] The invention relates to the field of fault diagnosis, in particular to a method for fault diagnosis of an aluminum electrolytic tank based on an optimized relative matrix of a characteristic subspace. Background technique [0002] The aluminum electrolytic cell is a complex and special metallurgical industrial equipment. Because it is affected by the coupling of various physical fields such as electric field, magnetic field, and temperature field in the cell, it is prone to damage to the cathode, floating carbon slag in the electrolyte, and fluctuations in the aluminum liquid. Abnormal slot conditions. If abnormal cell conditions cannot be accurately and timely diagnosed and the control strategy adjusted, the production efficiency and service life of the electrolytic cell will be seriously affected. However, due to the large number of state parameters of the aluminum electrolytic cell, the measurement values ​​are not easy to collect, and the paramet...

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Problems solved by technology

However, after this method normalizes the original matrix, the eigenvalues ​​of the covariance matrix are approximately equal in size, that is, the original random matrix is ​​geometrically "uniformly" distributed, and it is difficult to extract a representative principal component

The Chinese patent application document "Optimized Weight Relative Principal Component Analysis Method for Fault Diagnosis of Aluminum Electrolyzer" (publication number: CN103952724A) proposes a relative principal component analysis method with optimized weight for aluminum electrolytic cell fault diagnosis, which can be used The genetic algorithm generates an optimal relative transformation matrix, and through relativization, the "uniform" distribution is highlighted, so as to better extract representative principal components, thereby improving the accuracy of fault diagnosis of aluminum electrolytic cells , but this method does not take into account the nonlinear characteristics of the state parameters of the aluminum electrolytic cell, and is not the most effective method for practical applications

[0004] The defect of the existing technology is that the state parameters of the aluminum electrolytic cell are not considered to be nonlinear, and the accuracy of the actual aluminum electrolytic fault diagnosis needs to be improved.

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