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

• Published in: Science progress (1916) 2024-10, Vol.107 (4), p.368504241285770
• Main Authors: Gu, Xu-Ping, Shi, Xian-Jun
• Format: Article
• Language: English
• Subjects: Engineering & Technology
• ISSN: 0036-8504; 2047-7163; 2047-7163
• DOI: 10.1177/00368504241285770

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.