Multi-Scale Attention Entropy for Robust Fault Diagnosis and Fault Severity Estimation in Rotating Machines Without Prior Knowledge

Multi-Scale Attention Entropy for Robust Fault Diagnosis and Fault Severity Estimation in Rotating Machines Without Prior Knowledge | 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 Multi-Scale Attention Entropy for Robust Fault Diagnosis and Fault Severity Estimation in Rotating Machines Without Prior Knowledge Emadaldin Sh Khoram-Nejad, Abdolreza Ohadi, Farshad Almas-Ganj This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-7219047/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 Assessing the severity levels of faults in rotating machines is a critical endeavour within the industry, owing to the challenging nature of the noisy working environment and the subtle fault characteristics present in the acquired signals. In this study, a new feature extraction method named multi-scale attention entropy (MSAE), which is a combination of the attention entropy (AttnEn) and the multi-scale entropy (MSE) to extract more discriminative features from the signals, is introduced and investigated. A comparison between the MSAE and randomly selected feature vectors built from a set of 32 statistically and probabilistically features, with the same length, is made to show the performance and ability of the MSAE method. The comparison also includes consideration of the feature vector extracted from the multi-scale sample entropy (MSSE), which is the earliest version of the MSE. Subsequently, all ten feature vectors are input into a support vector machine (SVM) classifier for fault diagnosis and estimation of fault severities. Finally, the performance of the methods is compared for two scenarios, fault diagnosis (FD) and fault diagnosis and severity estimation (FD&SE), on two challengeable datasets. The first dataset, the Case Western Reserve University bearing (CWRU) dataset, is a bearing fault dataset, while the second one, the Korea Advanced Institute of Science and Technology (KAIST) dataset, is a rotor-bearing fault dataset. After twenty iterations, the MSAE-SVM model achieved an average FD accuracy of "99.58%±0.57%" for the CWRU dataset and "93.05%±0.66%" for the KAIST dataset. In addition, the FD&SE accuracy of the MSAE-SVM model for CWRU and KAIST datasets were \(\:\text{98.64\%±0.68\%}\) and \(\:\text{95.75\%±0.71\%}\) , respectively. According to the accuracy tolerance of the feature vectors results from the MSAE-SVM, which is lower than those of other feature vectors, the presented model is more robust in testing accuracy. The presented model is also free of prior knowledge classification and presents much higher mean accuracy among other models used for comparison. Mechanical Engineering Fault diagnosis Severity estimation Multi-Scale entropy Attention entropy Full Text Additional Declarations The authors declare no competing interests. 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-7219047","acceptedTermsAndConditions":true,"allowDirectSubmit":true,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":491155397,"identity":"152c2231-7ef7-455c-aab8-140db91d853b","order_by":0,"name":"Emadaldin Sh Khoram-Nejad","email":"","orcid":"https://orcid.org/0000-0002-4304-6661","institution":"Amirkabir University of Technology (Tehran Polytechnic)","correspondingAuthor":false,"prefix":"","firstName":"Emadaldin","middleName":"Sh","lastName":"Khoram-Nejad","suffix":""},{"id":491155398,"identity":"2e0279e1-edd1-411d-8722-59701a4b8515","order_by":1,"name":"Abdolreza Ohadi","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAAzUlEQVRIiWNgGAWjYLACxgYGxjb2BiiPmWgtPAcYGA6QpKVBIgGqhRDgb+B9wPBxh51sn+TjZ9IfGOzkGdiBIviAxAF2A8aZZ5KN26TTzCQOMCQbNjCzG+C35gAbAzNvG3Nim3QCSAtzAgMzG34d8hAt9Yltkse/AbXUE9ZiANFyOLFNggdky2HCWgyBWhhnth03buPJKbY4Y3DcsI2QFjmgFoaPbdWy89uPb7xRUVEtz89/DL8WBvkH7D+Q3MnAQMCOUTAKRsEoGAXEAADy0Des6il8RAAAAABJRU5ErkJggg==","orcid":"https://orcid.org/0000-0001-6514-4089","institution":"Amirkabir University of Technology (Tehran Polytechnic)","correspondingAuthor":true,"prefix":"","firstName":"Abdolreza","middleName":"","lastName":"Ohadi","suffix":""},{"id":491155399,"identity":"b790e09d-3fd8-4f9e-ad81-fd9d7b4954e8","order_by":2,"name":"Farshad Almas-Ganj","email":"","orcid":"https://orcid.org/0000-0003-2455-6903","institution":"Amirkabir University of Technology (Tehran Polytechnic)","correspondingAuthor":false,"prefix":"","firstName":"Farshad","middleName":"","lastName":"Almas-Ganj","suffix":""}],"badges":[],"createdAt":"2025-07-26 06:41:39","currentVersionCode":1,"declarations":{"humanSubjects":false,"vertebrateSubjects":false,"conflictsOfInterestStatement":false,"humanSubjectEthicalGuidelines":false,"humanSubjectConsent":false,"humanSubjectClinicalTrial":false,"humanSubjectCaseReport":false,"vertebrateSubjectEthicalGuidelines":false},"doi":"10.21203/rs.3.rs-7219047/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-7219047/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":87803983,"identity":"4fe8e737-51eb-4087-b2f4-3307e85e8555","added_by":"auto","created_at":"2025-07-29 08:12:51","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":795687,"visible":true,"origin":"","legend":"","description":"","filename":"ISAV2024Fulltext.pdf","url":"https://assets-eu.researchsquare.com/files/rs-7219047/v1_covered_5fe88b9f-e18a-4d6a-b6c7-cbc1beebf238.pdf"}],"financialInterests":"The authors declare no competing interests.","formattedTitle":"\u003cp\u003eMulti-Scale Attention Entropy for Robust Fault Diagnosis and Fault Severity Estimation in Rotating Machines Without Prior Knowledge\u003c/p\u003e","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":true,"hideJournal":true,"highlight":"","institution":"Amirkabir University of Technology","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":"Fault diagnosis, Severity estimation, Multi-Scale entropy, Attention entropy","lastPublishedDoi":"10.21203/rs.3.rs-7219047/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-7219047/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"\u003cp\u003eAssessing the severity levels of faults in rotating machines is a critical endeavour within the industry, owing to the challenging nature of the noisy working environment and the subtle fault characteristics present in the acquired signals. In this study, a new feature extraction method named multi-scale attention entropy (MSAE), which is a combination of the attention entropy (AttnEn) and the multi-scale entropy (MSE) to extract more discriminative features from the signals, is introduced and investigated. A comparison between the MSAE and randomly selected feature vectors built from a set of 32 statistically and probabilistically features, with the same length, is made to show the performance and ability of the MSAE method. The comparison also includes consideration of the feature vector extracted from the multi-scale sample entropy (MSSE), which is the earliest version of the MSE. Subsequently, all ten feature vectors are input into a support vector machine (SVM) classifier for fault diagnosis and estimation of fault severities. Finally, the performance of the methods is compared for two scenarios, fault diagnosis (FD) and fault diagnosis and severity estimation (FD\u0026amp;SE), on two challengeable datasets. The first dataset, the Case Western Reserve University bearing (CWRU) dataset, is a bearing fault dataset, while the second one, the Korea Advanced Institute of Science and Technology (KAIST) dataset, is a rotor-bearing fault dataset. After twenty iterations, the MSAE-SVM model achieved an average FD accuracy of \"99.58%\u0026plusmn;0.57%\" for the CWRU dataset and \"93.05%\u0026plusmn;0.66%\" for the KAIST dataset. In addition, the FD\u0026amp;SE accuracy of the MSAE-SVM model for CWRU and KAIST datasets were \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{98.64\\%\u0026plusmn;0.68\\%}\\)\u003c/span\u003e\u003c/span\u003e and \u003cspan class=\"InlineEquation\"\u003e\u003cspan class=\"mathinline\"\u003e\\(\\:\\text{95.75\\%\u0026plusmn;0.71\\%}\\)\u003c/span\u003e\u003c/span\u003e, respectively. According to the accuracy tolerance of the feature vectors results from the MSAE-SVM, which is lower than those of other feature vectors, the presented model is more robust in testing accuracy. The presented model is also free of prior knowledge classification and presents much higher mean accuracy among other models used for comparison.\u003c/p\u003e","manuscriptTitle":"Multi-Scale Attention Entropy for Robust Fault Diagnosis and Fault Severity Estimation in Rotating Machines Without Prior Knowledge","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-07-29 07:56:46","doi":"10.21203/rs.3.rs-7219047/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":"08869cb4-12ac-4c4f-8453-96161b0f1fe9","owner":[],"postedDate":"July 29th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"posted","subjectAreas":[{"id":52155413,"name":"Mechanical Engineering"}],"tags":[],"updatedAt":"2025-07-29T07:56:46+00:00","versionOfRecord":[],"versionCreatedAt":"2025-07-29 07:56:46","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-7219047","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-7219047","identity":"rs-7219047","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}