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Fire Control System Operation Status Assessment Based on Information Fusion: Case Study

机译：基于信息融合的消防系统运行状态评估：案例研究

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In traditional fault diagnosis strategies, massive and disordered data cannot be utilized effectively. Furthermore, just a single parameter is used for fault diagnosis of a weapons fire control system, which might lead to uncertainty in the results. This paper proposes an information fusion method in which rough set theory (RST) is combined with an improved Dempster–Shafer (DS) evidence theory to identify various system operation states. First, the feature information of different faults is extracted from the original data, then this information is used as the evidence of the state for a diagnosis object. By introducing RST, the extracted fault information is reduced in terms of the number of attributes, and the basic probability value of the reduced fault information is obtained. Based on an analysis of conflicts in the existing DS evidence theory, an improved conflict evidence synthesis method is proposed, which combines the improved synthesis rule and the conflict evidence weight allocation methods. Then, an intelligent evaluation model for the fire control system operation state is established, which is based on the improved evidence theory and RST. The case of a power supply module in a fire control computer is analyzed. In this case, the state grade of the power supply module is evaluated by the proposed method, and the conclusion verifies the effectiveness of the proposed method in evaluating the operation state of a fire control system.
机译：在传统的故障诊断策略中，无法有效利用海量且混乱的数据。此外，仅将单个参数用于武器火控系统的故障诊断，这可能导致结果不确定。本文提出了一种信息融合方法，其中将粗糙集理论（RST）与改进的Dempster-Shafer（DS）证据理论相结合，以识别各种系统运行状态。首先，从原始数据中提取不同故障的特征信息，然后将该信息用作诊断对象状态的证据。通过引入RST，减少了提取的故障信息的属性数量，并且获得了减少的故障信息的基本概率值。在对现有DS证据理论中的冲突进行分析的基础上，提出了一种改进的冲突证据综合方法，将改进的综合规则与冲突证据权重分配方法相结合。然后，基于改进证据理论和RST，建立了火控系统运行状态的智能评估模型。分析了火控计算机中电源模块的情况。在这种情况下，通过所提出的方法评估电源模块的状态等级，该结论验证了所提出的方法在评估火控系统的运行状态方面的有效性。

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