Open AccessEvaluation of Resilience of Battle Damage Equipment Based on BN-Cloud Model
Author(s) -
Mingchang Song,
Qin Shi,
Qianggao Hu,
Zai-Jin You,
Yadong Wang
Publication year - 2020
Publication title -
mathematical problems in engineering
Language(s) - English
Resource type - Journals
SCImago Journal Rank - 0.262
H-Index - 62
eISSN - 1026-7077
pISSN - 1024-123X
DOI - 10.1155/2020/6328176
Subject(s) - cloud computing , resilience (materials science) , bayesian network , computer science , transformation (genetics) , reliability engineering , probabilistic logic , state (computer science) , hierarchy , engineering , operations research , artificial intelligence , algorithm , biochemistry , chemistry , physics , gene , thermodynamics , operating system , economics , market economy
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.