In order to solve the problem of a lack of supportive means for evaluating the resilience of battle damage equipment, a Bayesian network cloud model is proposed to evaluate the resilience of battle damage equipment. The equipment functional features are analyzed to establish the equipment functional state evaluation model. Moreover, the samples of Bayesian network parameters training are obtained by inserting the results of battle damage simulation into the functional evaluation model. The simulation flow of parts state recovery probability is designed to determine the relationship between parts’ functional state and time. Based on the cloud model, the transformation model of functional state level probability to functional index is established. Hence, the equipment functional state level probability obtained by Bayesian network reasoning is transformed into a functional index and the transformation from uncertainty to certainty is realized. Considering self-propelled artillery as the object of resilience evaluation, the results of numerical examples show that by this method, the problem of equipment resilience evaluation can be effectively solved, and more information can be obtained by the accurate representation method compared to the traditional Bayesian network probabilistic evaluation results. This is greatly significant to the wartime maintenance support decision.

中文翻译：

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基于BN-Cloud模型的战损装备韧性评估

为了解决缺少作战装备评估能力的支持手段的问题，提出了一种贝叶斯网络云模型来评估作战装备的抵抗力。分析设备功能特征，建立设备功能状态评估模型。此外，通过将战损模拟结果插入功能评估模型中，获得贝叶斯网络参数训练的样本。设计零件状态恢复概率的仿真流程来确定零件功能状态与时间之间的关系。基于云模型，建立了功能状态水平概率向功能指标的转换模型。因此，通过贝叶斯网络推理获得的设备功能状态水平概率转化为功能指标，实现了从不确定性到确定性的转换。以自行火炮为弹力评估的对象，数值算例结果表明，与传统的贝叶斯方法相比，该方法可以有效地解决装备弹力评估的问题，并且可以通过精确表示法获得更多信息。网络概率评估结果。这对战时维护支持决策至关重要。数值算例结果表明，与传统的贝叶斯网络概率评估结果相比，该方法可以有效地解决设备的弹性评估问题，并且可以通过精确表示法获得更多的信息。这对战时维护支持决策至关重要。数值算例结果表明，与传统的贝叶斯网络概率评估结果相比，该方法可以有效地解决设备的弹性评估问题，并且可以通过精确表示法获得更多的信息。这对战时维护支持决策至关重要。