基于空间映射的电子设备综合诊断优化设计。

IF 2.6 4区 综合性期刊 Q2 MULTIDISCIPLINARY SCIENCES
Xu-Ping Gu, Xian-Jun Shi
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引用次数: 0

摘要

电子设备内部测试和故障信息的复杂性使其综合诊断成为设计设备可靠性时的一个挑战性问题。目前的综合诊断主要针对测试优化和测试资源优化进行分析。然而,这忽略了它们之间的联系。本文提出了一种基于空间映射原理的集成诊断优化设计策略,定量描述了它们之间的约束关系。通过构建测试空间、资源空间和故障空间之间的逻辑映射关系,建立综合诊断优化模型,并基于灰狼优化算法寻求最优测试配置和测试资源配置。本文使用七个高维基准函数和一个电子设备综合诊断模型来验证算法的效率。从算法的优化速度和准确性两方面对本文提出的算法与其他四种算法进行了比较。结果表明,经过综合诊断优化后的电子设备的关键故障检测率、故障检测率、故障隔离率和误报率分别为 100%、99.99%、98.99% 和 0.2993%。综合诊断优化后,设备的测试次数减少了 88.9%,测试成本节约了 89%。与其他算法相比,灰狼优化的优化效果最好,试验次数减少了 42%-55%,试验成本降低了 77.63%-83.91%。该策略不仅考虑了设备测试优化和测试资源优化,还在提高测试效率的同时大幅降低了测试成本。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
Integrated diagnosis optimization design of the electronic equipment based on spatial mapping.

The complexity of test and fault information within electronic devices makes their integrated diagnosis a challenging problem when designing equipment reliability. Current integrated diagnosis is analyzed for test optimization and test resource optimization. However, this neglects the connection between them. This paper proposes a design strategy for integrated diagnosis optimization based on the spatial mapping principle to quantitatively describe the constraint relationship between them. The integrated diagnosis optimization model is established by constructing the logical mapping relationship between test space, resource space, and fault space, and the optimal test configuration and test resource configuration are sought based on the grey wolf optimization algorithm. Seven high-dimensional benchmark functions and an integrated diagnosis model of electronic equipment are used to verify the efficiency of the algorithm proposed in this paper. The proposed algorithm is compared with the other four in terms of the algorithm's optimization speed and accuracy. The results indicate that the electronic equipment after integrated diagnosis optimization has critical fault detection, fault detection, fault isolation, and false alarm rates of 100%, 99.99%, 98.99%, and 0.2993%, respectively. After the integrated diagnosis optimization, the number of tests of the equipment is reduced by 88.9%, and the test cost is saved by 89%. Compared with the other algorithms, grey wolf optimization achieves the best optimization results, reduces the number of tests by 42%-55%, and decreases the test cost by 77.63%-83.91%. This strategy not only considers the test optimization of equipment and test resources optimization but also dramatically reduces the test cost while improving the test efficiency.

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来源期刊
Science Progress
Science Progress Multidisciplinary-Multidisciplinary
CiteScore
3.80
自引率
0.00%
发文量
119
期刊介绍: Science Progress has for over 100 years been a highly regarded review publication in science, technology and medicine. Its objective is to excite the readers'' interest in areas with which they may not be fully familiar but which could facilitate their interest, or even activity, in a cognate field.
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