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A fault detection method for FADS system based on interval-valued neutrosophic sets, belief rule base, and D-S evidence reasoning
Aerospace Science and Technology ( IF 5.0 ) Pub Date : 2021-04-27 , DOI: 10.1016/j.ast.2021.106758
Qianlei Jia , Jiayue Hu , Weiguo Zhang

Fault detection, with the characteristics of strong uncertainty and randomness, has always been one of the research hotspots in the field of aerospace. Considering that devices will inevitably encounter various unknown interference in the process of use, which greatly limits the performance of many traditional fault detection methods. Therefore, the main aim of this paper is to address this problem from the perspective of uncertainty and randomness of measurement signal. In information engineering, interval-valued neutrosophic sets (IVNSs), belief rule base (BRB), and Dempster-Shafer (D-S) evidence reasoning are always characterized by the strong ability in revealing uncertainty, but each has its drawbacks. As a result, the three theories are firstly combined in this paper to form a powerful fault detection algorithm. Besides, a series of innovations are proposed to improve the method, including a new score function based on p-norm for IVNSs and a new approach of calculating the similarity between IVNSs, which are both proved by authoritative prerequisites. To illustrate the effectiveness of the proposed method, flush air data sensing (FADS), a technologically advanced airborne sensor, is adopted in this paper. The aerodynamic model of FADS is analyzed in detail using knowledge of aerodynamics under subsonic and supersonic conditions, meanwhile, the high-precision model is established based on the aerodynamic database obtained from CFD software. For further confirming the validity and feasibility, a comparison with the methods based on parity equation, χ2 distribution, and information fusion method ordered weighted averaging (OWA) with three sets of weight vectors are conducted.



中文翻译:

基于区间值中智集,信念规则库和DS证据推理的FADS系统故障检测方法

具有强烈的不确定性和随机性的故障检测一直是航空航天领域的研究热点之一。考虑到设备在使用过程中不可避免地会遇到各种未知的干扰,这极大地限制了许多传统故障检测方法的性能。因此,本文的主要目的是从测量信号的不确定性和随机性的角度来解决这个问题。在信息工程中,区间值中性集(IVNS),信念规则库(BRB)和Dempster-Shafer(DS)证据推理始终具有揭示不确定性的强大能力,但每个都有其缺点。因此,本文首先将这三种理论结合起来,形成了一种功能强大的故障检测算法。除了,提出了一系列改进该方法的创新,包括针对IVNS的基于p范数的新评分函数和一种计算IVNS之间相似度的新方法,这两种方法均已得到权威先决条件的证明。为了说明所提方法的有效性,本文采用了技术先进的机载空气传感器空气数据感测(FADS)。利用亚音速和超音速条件下的空气动力学知识,对FADS的空气动力学模型进行了详细分析,同时,基于从CFD软件获得的空气动力学数据库,建立了高精度模型。为了进一步确认其有效性和可行性,将其与基于奇偶校验方程的方法进行了比较,包括一个基于p范数的IVNS的新评分函数,以及一种计算IVNS之间相似度的新方法,这都由权威先决条件证明。为了说明所提方法的有效性,本文采用了技术先进的机载空气传感器空气数据感测(FADS)。利用亚音速和超音速条件下的空气动力学知识,对FADS的空气动力学模型进行了详细分析,同时,基于从CFD软件获得的空气动力学数据库,建立了高精度模型。为了进一步确认其有效性和可行性,将其与基于奇偶校验方程的方法进行了比较,包括一个基于p范数的IVNS的新评分函数,以及一种计算IVNS之间相似度的新方法,这都由权威先决条件证明。为了说明所提方法的有效性,本文采用了技术先进的机载空气传感器空气数据感测(FADS)。利用亚音速和超音速条件下的空气动力学知识,对FADS的空气动力学模型进行了详细分析,同时,基于从CFD软件获得的空气动力学数据库,建立了高精度模型。为了进一步确认其有效性和可行性,将其与基于奇偶校验方程的方法进行了比较,本文采用了技术先进的机载空气传感器空气数据感测(FADS)。利用亚音速和超音速条件下的空气动力学知识,对FADS的空气动力学模型进行了详细分析,同时,基于从CFD软件获得的空气动力学数据库,建立了高精度模型。为了进一步确认其有效性和可行性,将其与基于奇偶校验方程的方法进行了比较,本文采用了技术先进的机载空气传感器空气数据感测(FADS)。利用亚音速和超音速条件下的空气动力学知识,对FADS的空气动力学模型进行了详细分析,同时,基于从CFD软件获得的空气动力学数据库,建立了高精度模型。为了进一步确认其有效性和可行性,将其与基于奇偶校验方程的方法进行了比较,χ2个 分布,并采用三组权向量进行信息融合方法有序加权平均(OWA)。

更新日期:2021-04-29
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