Equipment fault prediction method based on particle swarm optimization support vector regression

Abstract

The invention discloses an equipment fault prediction method based on particle swarm optimization support vector regression, and the method comprises the steps: carrying out the feature extraction ofvibration signal data in industrial production key equipment based on a wavelet decomposition method, and obtaining feature data; Secondly, constructing a time sequence of the feature data, selectingfirst n continuous feature data from the time sequence, and establishing a row number n-according to a set mapping dimension m; Wherein m + 1 is an input sample with the column number being m; And finally, carrying out fault prediction on the equipment by using the trained support vector regression model by using the input sample. According to the method, the particle swarm algorithm is adopted, and three key parameters of the support vector regression model are optimized at the same time, so that a feasible and efficient method is provided for optimizing the parameters of the support vector regression model, and the accuracy of predicting the equipment fault by using the support vector regression algorithm is improved.

Description

technical field [0001] The invention belongs to the field of equipment failure prediction, and more specifically relates to an equipment failure prediction method based on particle swarm optimization support vector regression. Background technique [0002] Nowadays, with the rapid development of computer control and information management technology, industrial production and manufacturing are being upgraded in the direction of large-scale, intelligent, and automated. If there is a failure in a link, it will lead to the failure of the system function, affect the normal production, cause heavy economic losses to the enterprise, and cause personnel safety accidents in severe cases, bringing losses to the country and the people. [0003] From the perspective of safe production and enterprise economic benefits, it is very necessary to predict the failure of the operation data of the key equipment of industrial production. Fault prediction is to predict the faults that will occu...

AI Technical Summary

Problems solved by technology

[0005] In view of the above defects or improvement needs of the prior art, the present invention provides a device failure prediction method based on particle swarm optimization support vector regression based on a data-driven prediction method, thereby solving the large deviation of the prediction results of the existing fault prediction method , relatively low prediction accuracy, and low optimization efficiency of the parameters of the prediction algorithm

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