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基于空间映射的电子设备综合诊断优化设计

Integrated diagnosis optimization design of the electronic equipment based on spatial mapping.

作者信息

Gu Xu-Ping, Shi Xian-Jun

机构信息

College of Coastal Defense Force, Naval Aviation University, Yantai, China.

出版信息

Sci Prog. 2024 Oct-Dec;107(4):368504241285770. doi: 10.1177/00368504241285770.

Abstract

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.

摘要

电子设备中测试和故障信息的复杂性使得在设计设备可靠性时,对其进行综合诊断成为一个具有挑战性的问题。针对测试优化和测试资源优化对当前的综合诊断进行了分析。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综合诊断优化设计策略,以定量描述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。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本文提出算法的有效性。将该算法与其他四种算法在优化速度和准确性方面进行了比较。结果表明,综合诊断优化后的电子设备关键故障检测率、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%,测试成本节省了89%。与其他算法相比,灰狼优化取得了最佳优化效果,测试次数减少了42%-55%,测试成本降低了77.63%-83.91%。该策略不仅考虑了设备的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。

https://cdn.ncbi.nlm.nih.gov/pmc/blobs/9b0d/11539195/4e9c2ac1a7fd/10.1177_00368504241285770-fig1.jpg

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