Considering the Life Prediction of Construction Machinery Workshop

Abstract

Traditionally, the replacement of products in construction machinery workshop is based on empirical cycles, although this strategy is simple, it cannot give full play to the full life of the products, resulting in underutilization, reduction of part accuracy, and increase of surface roughness. In order to solve the shortcomings of the existing research on life prediction of construction machinery workshop, this paper briefly discusses the selection of workshop construction machinery product samples and equipment parameter settings for the proposed life prediction model of construction machinery workshop based on the discussion of gated cyclic unit neural network and hydraulic motor life prediction criteria. And the design of the prediction model is discussed, and finally the prediction model is compared with the other two models for experimental analysis. The experimental data show that the prediction effect of the gated cyclic unit neural network is better than the other two models, and its prediction results are closer to the real life value. Its prediction accuracy reaches up to 94.8%. Therefore, it is verified that the gated recurrent unit neural network can make accurate prediction of the life time of the construction machinery workshop.