Data Simulation of Maintenance Material Demand for Armored Equipment

doi:10.12382/bgxb.2021.0113

Abstract

Abstract: The predictive effect of the big-data-driven prediction model on the consumption demand of equipment maintenance materials with small sample size is not ideal. A data simulation expansion algorithm is proposed. The AP clustering algorithm is improved by taking the accumulated equipment consumption at different time stages as similarity measure, and the data at different time stages are clustered by iteration. Each relative equipment consumption in the report stage, in which the data are clustered as the same category, is considered as normally distributed data. The normal distribution characteristics of each component of a vector to be predicted are calculated, and a large number of random samples are generated by using mathematical software as the simulation training samples of the data-driven prediction model. The numerical results show that the simulation training samples generated by this method can effectively improve the predictive effect of the big-data-driven prediction model with few samples, and with the increase in the number of simulation sample data, the predicted results of different prediction models are stable near the same predicted value, which effectively improves the credibility of the predicted results of the data-driven model.

Key words: equipmentdemandprediction, smallsamplesize, datasimulation, datamining

CLC Number:

ZHANG Lei, LI Shimin, KANG Shugui, WANG Tiening, GUO Mengchao. Data Simulation of Maintenance Material Demand for Armored Equipment[J]. Acta Armamentarii, 2022, 43(3): 720-728.

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