A Fault Diagnosis Method for Analog Circuits Based on VMD-CFOA-ELM

A Fault Diagnosis Method for Analog Circuits Based on VMD-CFOA-ELM | 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 Article A Fault Diagnosis Method for Analog Circuits Based on VMD-CFOA-ELM Shuangbao Ma, Songjie Shi, Li Wan, Yapeng Zhang, Guoqin Zhang This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5769828/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 Fault feature extraction of analog circuits is the main factor affecting their fault diagnosis accuracy. Therefore, in order to increase the accuracy of analog circuit fault diagnosis, this paper proposes a fault diagnosis method for analog circuit based on VMD-CFOA-ELM and DGCCA(Deep Generalized Canonical Correlation Analysis,DGCCA). The method firstly performs VMD(Variational Mode Decomposition,VMD) on the fault time-domain response signal to obtain all the IMF (Intrinsic Mode Function,IMF) components and calculates the CMSE(Composite Multi-Scale Entropy,CMSE) of each IMF component, treating this as a separate subset of fault features. Next, it extracts the time-frequency domain characteristic features of the fault response signal and uses the DGCCA algorithm for supervised feature fusion extraction, constructing a shared representation feature matrix G, which is spliced with the subset of fault features as the input features of the classifier. Finally, the CFOA(Catch Fish Optimization Algorithm, CFOA)algorithm is relied upon to optimize the ELM (Extreme Learning Machine,ELM) classifier to have good classification accuracy to diagnose the type of faults in the test circuits. Fault simulation diagnostics on a Sallen-Key bandpass filter circuit show that, compared to methods such as KPCA-ELM, KSLPP-ELM, EEMD multi-scale entropy-LSSVM, and WP-ICA energy spectrum-SVM, the proposed method achieves the highest accuracy, reaching 99.20%. Additionally, this method is portable and applicable to fault diagnosis of other analog circuits. Physical sciences/Engineering Physical sciences/Mathematics and computing Soft Fault Diagnosis Variational Mode Decomposition Deep Generalized CCA Catch Fish Optimization Algorithm Extreme Learning Machine Full Text Additional Declarations No competing interests reported. Supplementary Files dataandoriginalcode.rar 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. Our growing team is made up of researchers and industry professionals working together to solve the most critical problems facing scientific publishing. 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-5769828","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":400967660,"identity":"4bcda94d-a03b-4800-a626-2bc4b3a37aa8","order_by":0,"name":"Shuangbao Ma","email":"","orcid":"","institution":"Hubei Key Laboratory of Digital Textile Equipment, Wuhan Textile University","correspondingAuthor":false,"prefix":"","firstName":"Shuangbao","middleName":"","lastName":"Ma","suffix":""},{"id":400967661,"identity":"f535b399-970c-4434-8c5c-d00a592e0585","order_by":1,"name":"Songjie Shi","email":"","orcid":"","institution":"School of Mechanical 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Therefore, in order to increase the accuracy of analog circuit fault diagnosis, this paper proposes a fault diagnosis method for analog circuit based on VMD-CFOA-ELM and DGCCA(Deep Generalized Canonical Correlation Analysis,DGCCA). The method firstly performs VMD(Variational Mode Decomposition,VMD) on the fault time-domain response signal to obtain all the IMF (Intrinsic Mode Function,IMF) components and calculates the CMSE(Composite Multi-Scale Entropy,CMSE) of each IMF component, treating this as a separate subset of fault features. Next, it extracts the time-frequency domain characteristic features of the fault response signal and uses the DGCCA algorithm for supervised feature fusion extraction, constructing a shared representation feature matrix G, which is spliced with the subset of fault features as the input features of the classifier. Finally, the CFOA(Catch Fish Optimization Algorithm, CFOA)algorithm is relied upon to optimize the ELM (Extreme Learning Machine,ELM) classifier to have good classification accuracy to diagnose the type of faults in the test circuits. Fault simulation diagnostics on a Sallen-Key bandpass filter circuit show that, compared to methods such as KPCA-ELM, KSLPP-ELM, EEMD multi-scale entropy-LSSVM, and WP-ICA energy spectrum-SVM, the proposed method achieves the highest accuracy, reaching 99.20%. Additionally, this method is portable and applicable to fault diagnosis of other analog circuits.\u003c/p\u003e","manuscriptTitle":"A Fault Diagnosis Method for Analog Circuits Based on VMD-CFOA-ELM","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-01-13 02:24:23","doi":"10.21203/rs.3.rs-5769828/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":"265b8276-9132-40a9-a344-f32e3f4a6eca","owner":[],"postedDate":"January 13th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":42733787,"name":"Physical sciences/Engineering"},{"id":42733788,"name":"Physical sciences/Mathematics and computing"}],"tags":[],"updatedAt":"2025-03-31T03:38:54+00:00","versionOfRecord":[],"versionCreatedAt":"2025-01-13 02:24:23","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5769828","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5769828","identity":"rs-5769828","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}