系统工程与电子技术 ›› 2020, Vol. 42 ›› Issue (4): 878-886.doi: 10.3969/j.issn.1001-506X.2020.04.19

• 系统工程 • 上一篇    下一篇

基于时间序列挖掘的合成旅装备维修保障能力预测

宋星(), 贾红丽(), 王谦(), 赵汝东()   

  1. 陆军工程大学石家庄校区装备指挥与管理系, 河北 石家庄 050003
  • 收稿日期:2019-07-01 出版日期:2020-03-28 发布日期:2020-03-28
  • 作者简介:宋星(1991-),男,硕士研究生,主要研究方向为装备管理理论与应用。E-mail:flying506@163.com|贾红丽(1972-),女,副教授,硕士研究生导师,博士,主要研究方向为装备管理理论与应用。E-mail:13731129566@163.com|王谦(1989-),男,硕士研究生,主要研究方向为装备保障理论与应用,可靠性工程。E-mail:18003131595@163.com|赵汝东(1995-),男,硕士研究生,主要研究方向为系统管理理论与应用。E-mail:1099248290@qq.com
  • 基金资助:
    军内科研计划项目(012016012600B12102)

Prediction of equipment maintenance support capability of synthetic brigade based on time series mining

Xing SONG(), Hongli JIA(), Qian WANG(), Rudong ZHAO()   

  1. Department of Equipment Command and Management, Shijiazhuang Campus, Army Engineering University, Shijiazhuang 050003, China
  • Received:2019-07-01 Online:2020-03-28 Published:2020-03-28
  • Supported by:
    军内科研计划项目(012016012600B12102)

摘要:

针对现有的预测方法参数较多、精确度不高的问题,采用了时间序列挖掘的方法对合成旅未来一定时期内的装备维修保障能力进行预测。首先建立了指标体系,利用“装备云”平台相关数据对指标及装备维修保障能力随时间变化的序列进行计算;然后对多元时间序列进行线段化拟合、聚类、符号化表达、Apriori关联挖掘,通过差分整合移动平均自回归-支持向量回归组合模型及反向传播神经网络对合成旅装备维修保障能力进行预测,最后通过事例验证了本文所提出的方法。

关键词: 时间序列挖掘, 合成旅, 装备维修保障, Apriori算法, 差分整合移动平均自回归, 支持向量回归机, 预测

Abstract:

In view of the problem that there are too many parameters in the existing prediction method and the accuracy is not high, the time series mining method is used to predict the equipment maintenance support capability of the synthetic brigade in a certain period of time in the future. Firstly, the index system is established and the data of the "equipment cloud" platform is used to calculate the sequence of indicators and equipment maintenance support ability. Then, the segmentation fitting, clustering, symbolic expression and Apriori association mining are applied to the multivariate time series. The autoregressive integrated moving average-support vector regression (ARIMA-SVR) model and back propagation (BP) neural network are used to predict the equipment maintenance support capability of the synthetic brigade. Finally, the proposed method is verified by an example.

Key words: time series mining, synthetic brigade, equipment maintenance support, Apriori algorithm, autoregressive integrated moving average (ARIMA), support vector regression (SVR), prediction

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