Improvement of Generalization Performance of Diagnostic System for Drill Bit Abnormality in Rotary Percussion Drilling with Grad-CAM

Improvement of Generalization Performance of Diagnostic System for Drill Bit Abnormality in Rotary Percussion Drilling with Grad-CAM | 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 Improvement of Generalization Performance of Diagnostic System for Drill Bit Abnormality in Rotary Percussion Drilling with Grad-CAM Yuna Nakazawa, Natsuo Okada, Jo Sasaki, Lesego Senjoba, Yoko Ohtomo, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5162718/v1 This work is licensed under a CC BY 4.0 License Status: Under Review Version 1 posted 11 You are reading this latest preprint version Abstract Rotary percussion drills, specifically top hammer types, are essential in resource extraction and exploration. These drills frequently suffer damage at the drill button, necessitating effective decision-making to address issues. Traditionally, on-site operators relied on intuition, but recent advancements promote automated systems to enhance safety and failure detection in challenging mining environments. This study employs Convolutional Neural Networks (CNN) to develop an automated system for detecting drill bit abnormalities. In a prior investigation, a CNN model was established to discern normal functioning and four distinct abnormalities. To enhance generalization performance, researchers endeavored to create a filter using Gradient-weighted Class Activation Mapping (Grad-CAM), an inverse analysis technique. However, challenges arose as the frequency bands contributing significantly to judgments varied among the five identified states. In response, this study adopts a refined approach, constructing a model that categorizes drill performance into two states: normal and abnormal. Leveraging Grad-CAM, the research identifies the frequency band with a high contribution rate to judgment and applies a filter tailored to this specific band. Notably, by training the CNN model to recognize differences in rock types, the resulting model exhibits adaptability to changes in drill bit types, achieving an accuracy rate of 83.3%. Rotary percussion drill Convolutional Neural Network Grad-CAM Drill bit abnormality Frequency filter Mining automation Full Text Additional Declarations No competing interests reported. Cite Share Download PDF Status: Under Review Version 1 posted Editorial decision: Revision requested 15 Oct, 2024 Reviews received at journal 14 Oct, 2024 Reviews received at journal 14 Oct, 2024 Reviews received at journal 10 Oct, 2024 Reviewers agreed at journal 10 Oct, 2024 Reviewers agreed at journal 08 Oct, 2024 Reviewers agreed at journal 08 Oct, 2024 Reviewers invited by journal 08 Oct, 2024 Editor assigned by journal 07 Oct, 2024 Submission checks completed at journal 04 Oct, 2024 First submitted to journal 27 Sep, 2024 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-5162718","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":366165022,"identity":"67900b21-7272-4bb6-8f1c-1e69bd0c30d8","order_by":0,"name":"Yuna Nakazawa","email":"data:image/png;base64,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","orcid":"","institution":"Hokkaido University","correspondingAuthor":true,"prefix":"","firstName":"Yuna","middleName":"","lastName":"Nakazawa","suffix":""},{"id":366165024,"identity":"d48c8179-620e-4ff9-9dab-29a932f76c66","order_by":1,"name":"Natsuo Okada","email":"","orcid":"","institution":"Hokkaido University","correspondingAuthor":false,"prefix":"","firstName":"Natsuo","middleName":"","lastName":"Okada","suffix":""},{"id":366165025,"identity":"0db22909-57a7-499d-bab8-d40cfb990897","order_by":2,"name":"Jo Sasaki","email":"","orcid":"","institution":"MMC RYOTEC Co., Ltd","correspondingAuthor":false,"prefix":"","firstName":"Jo","middleName":"","lastName":"Sasaki","suffix":""},{"id":366165026,"identity":"a569652a-0789-4114-aaf4-65739a01fe0d","order_by":3,"name":"Lesego Senjoba","email":"","orcid":"","institution":"Akita University","correspondingAuthor":false,"prefix":"","firstName":"Lesego","middleName":"","lastName":"Senjoba","suffix":""},{"id":366165028,"identity":"b242d7c0-d5f6-4fa4-a79a-d77074e644c4","order_by":4,"name":"Yoko Ohtomo","email":"","orcid":"","institution":"Hokkaido University","correspondingAuthor":false,"prefix":"","firstName":"Yoko","middleName":"","lastName":"Ohtomo","suffix":""},{"id":366165029,"identity":"93edb729-8976-4ad4-809c-9ae0b1b57a41","order_by":5,"name":"Youhei Kawamura","email":"","orcid":"","institution":"Hokkaido University","correspondingAuthor":false,"prefix":"","firstName":"Youhei","middleName":"","lastName":"Kawamura","suffix":""}],"badges":[],"createdAt":"2024-09-27 06:23:52","currentVersionCode":1,"declarations":"","doi":"10.21203/rs.3.rs-5162718/v1","doiUrl":"https://doi.org/10.21203/rs.3.rs-5162718/v1","draftVersion":[],"editorialEvents":[],"editorialNote":"","failedWorkflow":false,"files":[{"id":68373112,"identity":"dd1bf6dd-5c05-4971-8e0d-1892ec2c0e88","added_by":"auto","created_at":"2024-11-06 14:38:59","extension":"pdf","order_by":1,"title":"","display":"","copyAsset":false,"role":"manuscript-pdf","size":900977,"visible":true,"origin":"","legend":"","description":"","filename":"DiscoverAppliedSciencesYunaNakazawa.pdf","url":"https://assets-eu.researchsquare.com/files/rs-5162718/v1_covered_bb0de2d3-92b1-4779-93d7-7b581bd868f8.pdf"}],"financialInterests":"No competing interests reported.","formattedTitle":"Improvement of Generalization Performance of Diagnostic System for Drill Bit Abnormality in Rotary Percussion Drilling with Grad-CAM","fulltext":[],"fulltextSource":"","fullText":"","funders":[],"hasAdminPriorityOnWorkflow":false,"hasManuscriptDocX":false,"hasOptedInToPreprint":true,"hasPassedJournalQc":"","hasAnyPriority":false,"hideJournal":false,"highlight":"","institution":"","isAcceptedByJournal":true,"isAuthorSuppliedPdf":true,"isDeskRejected":"","isHiddenFromSearch":false,"isInQc":false,"isInWorkflow":false,"isPdf":true,"isPdfUpToDate":true,"isWithdrawnOrRetracted":false,"journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true},"keywords":"Rotary percussion drill, Convolutional Neural Network, Grad-CAM, Drill bit abnormality, Frequency filter, Mining automation","lastPublishedDoi":"10.21203/rs.3.rs-5162718/v1","lastPublishedDoiUrl":"https://doi.org/10.21203/rs.3.rs-5162718/v1","license":{"name":"CC BY 4.0","url":"https://creativecommons.org/licenses/by/4.0/"},"manuscriptAbstract":"Rotary percussion drills, specifically top hammer types, are essential in resource extraction and exploration. These drills frequently suffer damage at the drill button, necessitating effective decision-making to address issues. Traditionally, on-site operators relied on intuition, but recent advancements promote automated systems to enhance safety and failure detection in challenging mining environments. This study employs Convolutional Neural Networks (CNN) to develop an automated system for detecting drill bit abnormalities. In a prior investigation, a CNN model was established to discern normal functioning and four distinct abnormalities. To enhance generalization performance, researchers endeavored to create a filter using Gradient-weighted Class Activation Mapping (Grad-CAM), an inverse analysis technique. However, challenges arose as the frequency bands contributing significantly to judgments varied among the five identified states. In response, this study adopts a refined approach, constructing a model that categorizes drill performance into two states: normal and abnormal. Leveraging Grad-CAM, the research identifies the frequency band with a high contribution rate to judgment and applies a filter tailored to this specific band. Notably, by training the CNN model to recognize differences in rock types, the resulting model exhibits adaptability to changes in drill bit types, achieving an accuracy rate of 83.3%.","manuscriptTitle":"Improvement of Generalization Performance of Diagnostic System for Drill Bit Abnormality in Rotary Percussion Drilling with Grad-CAM","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2024-11-06 14:38:34","doi":"10.21203/rs.3.rs-5162718/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Revision requested","date":"2024-10-15T07:43:18+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-14T16:58:12+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-14T05:59:26+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2024-10-10T19:25:22+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"224589019865032239373680474658510193019","date":"2024-10-10T04:55:37+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"85885931400028115464958847476098288693","date":"2024-10-08T05:07:02+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"311532141489027824572318143260934424582","date":"2024-10-08T04:55:10+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2024-10-08T04:50:22+00:00","index":"","fulltext":""},{"type":"editorAssigned","content":"","date":"2024-10-07T13:05:06+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2024-10-04T10:09:12+00:00","index":"","fulltext":""},{"type":"submitted","content":"Discover Applied Sciences","date":"2024-09-27T06:13:37+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"discover-applied-sciences","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"","sideBox":"Learn more about [Discover Applied Sciences](https://link.springer.com/journal/42452)","snPcode":"42452","submissionUrl":"https://submission.springernature.com/new-submission/42452/3","title":"Discover Applied Sciences","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Discover Series","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"36910bda-634c-44e3-9145-5825e1a7d9e3","owner":[],"postedDate":"November 6th, 2024","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"under-review","subjectAreas":[],"tags":[],"updatedAt":"2025-03-25T03:23:09+00:00","versionOfRecord":[],"versionCreatedAt":"2024-11-06 14:38:34","video":"","vorDoi":"","vorDoiUrl":"","workflowStages":[]},"version":"v1","identity":"rs-5162718","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5162718","identity":"rs-5162718","version":["v1"]},"buildId":"qtupq5eGEP_6zYnWcrvyt","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}