Fault prediction method in industrial production based on particle swarm optimization support vector regression

Abstract

The invention discloses a fault prediction method in industrial production based on particle swarm optimization support vector regression, and the method comprises the steps: calculating an average deviation and a variance, carrying out the feature extraction of multi-dimensional data in an industrial production process, and obtaining the feature data of an original input sample set; Constructinga time sequence of feature data of the original input sample set, selecting previous h continuous feature data from the time sequence, and establishing a row number h-according to a set mapping dimension m; Wherein m + 1 is an input sample with the column number being m; And carrying out fault prediction on the industrial production process by using an input sample and a trained support vector regression model. According to the method, the particle swarm algorithm is adopted, three key parameters of the support vector regression model are optimized at the same time, a feasible and efficient method is provided for optimization of the parameters of the support vector regression model, and the accuracy of fault prediction in the industrial production process through the support vector regression algorithm is improved.

Description

technical field [0001] The invention belongs to the field of fault prediction of industrial production process, and more specifically relates to a fault prediction method of industrial production process based on particle swarm optimization support vector regression. Background technique [0002] The industrial production process system is becoming more and more complex, and the various process procedures are interrelated and affect each other. Once any of the industrial production processes or links fail, it will lead to system function failure, affect normal production, cause major economic losses to the enterprise, and in severe cases It will cause personnel safety accidents and bring losses to the country and the people. Therefore, from the perspective of safety production and enterprise economic benefits, it is very necessary to predict failures through the operation data of key industrial production processes in industrial production. [0003] Fault prediction is to p...

AI Technical Summary

Problems solved by technology

[0004] Aiming at the above defects or improvement needs of the prior art, the present invention provides a fault prediction method for industrial production processes based on particle swarm optimization support vector regression, thereby solving the problems of large deviations in prediction results and relatively low prediction accuracy of existing fault prediction methods. Low and low efficiency of parameter optimization of the prediction algorithm

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