基于D-S证据理论的装备维修性多源数据融合方法

Multi-source Fusion of Equipment Maintenance Data Based on D-S Evidence Theory

针对装备维修性评估时现场数据样本量不足的问题,建立一种基于D-S证据理论的多源数据融合方法,充分利用装备前期各阶段或其他来源的维修性试验数据。首先对传统的Bayes Bootstrap法进行改进,较为精确地拟合离散化的各源维修性试验数据的分布参数,再从数据源中挖掘样本量和分布特征等信息构建证据,采用D-S理论合成证据作为权重,建立维修性多源数据融合模型,最后采用某坦克的维修性试验数据进行实例分析,验证了该融合方法的有效性。

Aiming at the shortage of on-site data samples in equipment maintenance evaluation,a multi-source data fusion method based on D-S evidence theory is established to make full use of the maintenance test data of the early stages or from other sources.Firstly,the traditional Bayes Bootstrap method is improved.The discretized maintenance test data from each source is more accurately fitted to its distribution parameters,then the information such as sample size and distribution characteristics is mined from the data source to construct an evidence.The evidence synthesized by D-S evidence theory is taken as a weight for constructing a multi-source data fusion model of maintenance.Finally,the maintenance test data of a certain type of tank is used to analyze the example,and the effectiveness of the fusion method is verified.