Evidence map›Paper›PMID 39501583›Full record

ArticleScience progress

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

Xu-Ping Gu, Xian-Jun Shi

Abstract read
In one paragraph

Article in Science progress. The graph could read no effect estimate from its abstract, so it casts no vote on the map. Not yet cited in PubMed.

0numbers the graph read from it
0cells of the map it votes in
0citing papers in PubMed
–field-weighted citation impact
1 · What the graph read from it

What it found

Each row is one number read from the abstract, on the scale the paper reported it, with its interval. Left of the dashed line favours the treatment, right favours the comparator. Under each row is the sentence it came from. New to these charts? A ten-minute tutorial.

The abstract states no effect estimate the extractor could read, or names no intervention and outcome on the map, so this paper lights no cell and moves no belief. It is still indexed, cited and linked below.

2 · The registry

The trial behind it

Trials whose registry record cites this paper, or whose number appears in the abstract. A trial that started after this paper was published is citing it as background, not reporting it.

Neither the registry nor the abstract names a trial number. If this is a trial report, that itself is worth knowing.

3 · Its place in the literature

Who cites it

0 citing papers in PubMed.

No citing paper in PubMed yet.

4 · The record

Corrections and comments

PubMed lists nothing against this paper. Absence here is not a guarantee, only a check that was made.

5 · Who and what money

Authors and funding

2 authors.

Xu-Ping GuCollege of Coastal Defense Force, Naval Aviation University, Yantai, China.ORCID 0009-0009-4067-7626
Xian-Jun ShiCollege of Coastal Defense Force, Naval Aviation University, Yantai, China.

Funding

No grant is acknowledged in the PubMed record.

6 · The paper itself

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.

Indexed as

gray wolf optimization algorithmhierarchical analysisIntegrated diagnosisresource optimizationtest optimization

Identifiers

PMID39501583
PMCPMC11539195

What OpenQuestion holds

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LicenceCC BY-NC
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Registered trials

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Read under generation 80e0d062 · epoch 390. Bibliography from PubMed, PubMed Central and OpenAlex; grants from NIH RePORTER; trial links from ClinicalTrials.gov; estimates, votes and beliefs from the OpenQuestion graph.