An AEALSCE-based optimization method of sequential diagnostic strategy generation

An AEALSCE-based optimization method of sequential diagnostic strategy generation | Research Square window.SnipcartSettings = { analytics: { enabled: false } }; (function() { var accessVector = localStorage.getItem('access_vector') || ''; window.dataLayer = window.dataLayer || []; if (accessVector) { window.dataLayer.push({ user: { profile: { profileInfo: { snid: accessVector } } } }); } })(); (function(w,d,s,l,i){w[l]=w[l]||[];w[l].push({'gtm.start':new Date().getTime(),event:'gtm.js'});var f=d.getElementsByTagName(s)[0],j=d.createElement(s),dl=l!='dataLayer'?'&l='+l:'';j.async=true;j.src='https://www.googletagmanager.com/gtm.js?id='+i+dl;f.parentNode.insertBefore(j,f);})(window,document,'script','dataLayer','GTM-K279D39R'); Browse Preprints In Review Journals COVID-19 Preprints AJE Video Bytes Research Tools Research Promotion AJE Professional Editing AJE Rubriq About Preprint Platform In Review Editorial Policies Our Team Advisory Board Help Center Sign In Submit a Preprint Cite Share Download PDF Research Article An AEALSCE-based optimization method of sequential diagnostic strategy generation Yajun Liang, Hongliang Ji, Guicai Fang, Yang Yang, Xiaofei Wang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7337679/v1 This work is licensed under a CC BY 4.0 License Status: Posted Version 1 posted You are reading this latest preprint version Abstract As an important part of testability design and fault diagnosis, sequential fault diagnostic strategy generation plays a crucial role in improving the efficiency of system fault diagnosis and reducing the cost of system maintenance support. With the system complexity increasing, it is extremely difficult to generate an optimal diagnostic strategy. The existing methods are easy to fall into the local optimal solution and the time complexity is high. Therefore, this paper proposes a method based on a covariance matrix adaptation evolution strategy (CMA-ES) variant, named AEALSCE, to obtain the optimal strategy. Firstly, the fault diagnosis strategy problem model based on AEALSCE is established, and the fault diagnostic strategy problem is transformed into a continuous optimization problem to adapt the algorithm solving. Then, based on the diagnostic objective of the minimum diagnostic steps and test cost, the fitness function of the algorithm is constructed. At last, the optimal diagnosis strategy is obtained by solving the transformed continuous optimization problem with AEALSCE algorithm. The experimental results proves that the proposed AEALSCE-based approach is reasonable and feasible in obtaining the optimal diagnostic strategy, and outperforms other traditional methods and evolutionary algorithms in terms of accuracy and time efficiency. Covariance matrix adaptation evolution strategy (CMA-ES) Testability design Diagnostic strategy Evolutionary computation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Posted Version 1 posted You are reading this latest preprint version Research Square lets you share your work early, gain feedback from the community, and start making changes to your manuscript prior to peer review in a journal. As a division of Research Square Company, we’re committed to making research communication faster, fairer, and more useful. We do this by developing innovative software and high quality services for the global research community. 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Also discoverable on Platform About Our Team In Review Editorial Policies Advisory Board Help Center Resources Author Services Accessibility API Access RSS feed Manage Cookie Preferences © Research Square 2026 | ISSN 2693-5015 (online) Privacy Policy Terms of Service Do Not Sell My Personal Information {"props":{"pageProps":{"initialData":{"identity":"rs-7337679","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":600726735,"identity":"f75bc406-d34d-474e-85f1-7f510d9022d7","order_by":0,"name":"Yajun Liang","email":"","orcid":"","institution":"China Aerodynamics Research and Development Center","correspondingAuthor":false,"prefix":"","firstName":"Yajun","middleName":"","lastName":"Liang","suffix":""},{"id":600726736,"identity":"a227892f-f998-4f44-97dc-1423f2852afd","order_by":1,"name":"Hongliang Ji","email":"","orcid":"","institution":"China Aerodynamics Research and Development Center","correspondingAuthor":false,"prefix":"","firstName":"Hongliang","middleName":"","lastName":"Ji","suffix":""},{"id":600726737,"identity":"bcee08b3-0696-4396-9bd9-7814df902790","order_by":2,"name":"Guicai Fang","email":"","orcid":"","institution":"China Aerodynamics Research and Development Center","correspondingAuthor":false,"prefix":"","firstName":"Guicai","middleName":"","lastName":"Fang","suffix":""},{"id":600726738,"identity":"4cc0eea9-9a7f-46df-9591-8b1419aa140e","order_by":3,"name":"Yang Yang","email":"","orcid":"","institution":"China Aerodynamics Research and Development Center","correspondingAuthor":false,"prefix":"","firstName":"Yang","middleName":"","lastName":"Yang","suffix":""},{"id":600726739,"identity":"fed54989-6c35-4229-bf1a-fecc6b41750e","order_by":4,"name":"Xiaofei Wang","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA8klEQVRIie3PMUoDQRTG8TcMvG0mazthJGd4y8KQYklylMjAVCks7RwQYhNImzR6BUvLTYYkzRwghcVKwDpiY7GgB1B2185ifvX3Lz6AKPqX2Gv1XuMAk+VHdaZi1CHhebZ2aZ6KUmera2u6JFr13ODqQU5IifOWubaAjoZT71mzuYRpXlDJIfG7p8YkeFb1g+WoXHma0UsKwtpjU6IPS0+Ee8TLjctn9MZBCt2ceI5yil8CpQE1JM9ca3K4Q7mZo0RpQUGXZBw8z1xAQhEwW5A12PalvzLsVN/g7eP9glefdTG6SPy+MfkJ/zaPoiiKfvMNPqNJkT6sDMkAAAAASUVORK5CYII=","orcid":"","institution":"Beijing BlueSky Innovation Center for Frontier Science","correspondingAuthor":true,"prefix":"","firstName":"Xiaofei","middleName":"","lastName":"Wang","suffix":""}],"badges":[],"createdAt":"2025-08-10 08:38:08","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-7337679/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7337679/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":103962776,"identity":"a37fe4e0-9b36-4456-804c-b9e4510c91df","added_by":"auto","created_at":"2026-03-05 05:10:29","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":709015,"visible":true,"origin":"","legend":"","description":"","filename":"AnAEALSCEbasedoptimizationmethod.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7337679/v1_covered_129792cb-99b7-4485-81be-2a460053bff6.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"An AEALSCE-based optimization method of sequential diagnostic strategy generation","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":true,"highlight":"","institution":"","isAcceptedByJournal":false,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true},"keywords":"Covariance matrix adaptation evolution strategy (CMA-ES), Testability design, Diagnostic strategy, Evolutionary computation","lastPublishedDoi":"10.21203/rs.3.rs-7337679/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7337679/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAs an important part of testability design and fault diagnosis, sequential fault diagnostic strategy generation plays a crucial role in improving the efficiency of system fault diagnosis and reducing the cost of system maintenance support. With the system complexity increasing, it is extremely difficult to generate an optimal diagnostic strategy. The existing methods are easy to fall into the local optimal solution and the time complexity is high. Therefore, this paper proposes a method based on a covariance matrix adaptation evolution strategy (CMA-ES) variant, named AEALSCE, to obtain the optimal strategy. Firstly, the fault diagnosis strategy problem model based on AEALSCE is established, and the fault diagnostic strategy problem is transformed into a continuous optimization problem to adapt the algorithm solving. Then, based on the diagnostic objective of the minimum diagnostic steps and test cost, the fitness function of the algorithm is constructed. At last, the optimal diagnosis strategy is obtained by solving the transformed continuous optimization problem with AEALSCE algorithm. The experimental results proves that the proposed AEALSCE-based approach is reasonable and feasible in obtaining the optimal diagnostic strategy, and outperforms other traditional methods and evolutionary algorithms in terms of accuracy and time efficiency.\u003c/p\u003e","manuscriptTitle":"An AEALSCE-based optimization method of sequential diagnostic strategy generation","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2026-03-05 05:10:17","doi":"10.21203/rs.3.rs-7337679/v1","editorialEvents":[{"type":"communityComments","content":0}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"researchsquare","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":true,"externalIdentity":"","sideBox":"","snPcode":"","submissionUrl":"/submission","title":"Research Square","twitterHandle":"researchsquare","acdcEnabled":true,"dfaEnabled":false,"editorialSystem":"","reportingPortfolio":"","inReviewEnabled":false,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"172e393d-8e5e-473f-9ae6-6f9c4920fc3d","owner":[],"postedDate":"March 5th, 2026","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[],"tags":[],"updatedAt":"2026-04-19T23:23:05+00:00","versionOfRecord":[],"versionCreatedAt":"2026-03-05 05:10:17","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7337679","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7337679","identity":"rs-7337679","version":["v1"]},"buildId":"XKTyCvWXoU3ODBz1xrDgd","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}