基于属性可靠性通用信念规则库的新型航空继电器健康状态评估方法

IF 7.2 1区 计算机科学 Q1 COMPUTER SCIENCE, ARTIFICIAL INTELLIGENCE Applied Soft Computing Pub Date : 2024-08-26 DOI:10.1016/j.asoc.2024.112135
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引用次数: 0

摘要

作为一种经典的基于规则的建模方法,信念规则库(BRB)专家系统能够整合专家知识,并具有良好的可解释性。建立在信念规则库基础上的具有属性可靠性的信念规则库(BRB-r)为解决模型可靠性和环境干扰问题提供了有效途径。此外,稳健性是衡量抗干扰能力的重要指标,稳健的 BRB-r 可以在各种环境中保持可靠和稳定。因此,为了提高模型的抗扰动能力,增强模型的适应性,我们开发了一种新的具有属性可靠性的通用 BRB(G-BRB-r)。具体而言,本文分析了 BRB-r 的鲁棒性,探讨了不同扰动下 BRB-r 鲁棒性的变化。此外,结合不同因素对鲁棒性的影响,给出了鲁棒 BRB-r 的构建标准和约束条件,以指导建模。然后,考虑到属性可靠性和鲁棒性对建模性能的影响,开发了一种具有属性可靠性的新型通用 BRB。最后,通过对航空航天继电器健康状态评估的案例研究,证明了所提方法的有效性和适应性。
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A new aeronautical relay health state assessment method based on generic belief rule base with attribute reliability

As a classical rule-based modeling approach, belief rule base (BRB) expert system can integrate expert knowledge and possesses good interpretability. BRB with attribute reliability (BRB-r), built upon BRB, provides an effective way to deal with the problems of model reliability and environmental disturbances. Moreover, robustness is an important measure of perturbation resistance, and a robust BRB-r can remain reliable and stable in various environments. Therefore, to improve the model's ability to resist perturbations and enhance the model's adaptability, a new generic BRB with attribute reliability (G-BRB-r) is developed. Specifically, the robustness of BRB-r is analyzed in this paper to explore the change of BRB-r robustness under different perturbations. In addition, combining the effects of different factors on robustness, the construction criteria and constraints of robust BRB-r are given to guide modeling. Then, considering the effects of attribute reliability and robustness on modeling performance, a new generic BRB with attribute reliability is developed. Finally, the effectiveness and adaptability of the proposed method are demonstrated through a case study for health state assessment of the aerospace relay.

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来源期刊
Applied Soft Computing
Applied Soft Computing 工程技术-计算机:跨学科应用
CiteScore
15.80
自引率
6.90%
发文量
874
审稿时长
10.9 months
期刊介绍: Applied Soft Computing is an international journal promoting an integrated view of soft computing to solve real life problems.The focus is to publish the highest quality research in application and convergence of the areas of Fuzzy Logic, Neural Networks, Evolutionary Computing, Rough Sets and other similar techniques to address real world complexities. Applied Soft Computing is a rolling publication: articles are published as soon as the editor-in-chief has accepted them. Therefore, the web site will continuously be updated with new articles and the publication time will be short.
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