MWGANN Prediction Model for Electromechanical Equipment Running State

Xiao Li Xu; Zhang Lei Jiang; Peng Chen

doi:10.4028/www.scientific.net/AMR.490-495.437

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HomeAdvanced Materials ResearchAdvanced Materials Research Vols. 490-495MWGANN Prediction Model for Electromechanical...

MWGANN Prediction Model for Electromechanical Equipment Running State

Abstract:

To ensure the rotary electromechanical equipment injection pump unit safe and stable operation, status assessment parameters should be predicted used by appropriate prediction model. This paper presents a genetic algorithm optimization neural network prediction model based on mean function new information-weighted theory (MWGANN prediction model). MWGANN prediction model can optimize the neural network structure parameters and improve the prediction accuracy and prediction timeliness by using the recency difference of time series data. Collecting large rotating injection pump unit vibration intensity time series in the industrial site, MWGANN prediction model and GANN prediction model are applied to predict trend. The results show that MWGANN model achieved good results in prediction accuracy and prediction timeliness.

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Periodical:

Advanced Materials Research (Volumes 490-495)

Pages:

437-441

DOI:

https://doi.org/10.4028/www.scientific.net/AMR.490-495.437

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Online since:

March 2012

Authors:

Xiao Li Xu, Zhang Lei Jiang, Peng Chen

Keywords:

Genetic Algorithm (GA), Mean Function, Neural Network (NN), New Information-Weighted, Prediction Model, Rotating Machinery

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References

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