Research on scraper conveyor load prediction method based on wavelet transform and BP neural network

Research on scraper conveyor load prediction method based on wavelet transform and BP neural network | 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 Research on scraper conveyor load prediction method based on wavelet transform and BP neural network Dan Zhang, Jiafeng Qin, Weidong Wu, Yongtao Zhu, Weijie Guo This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-5440016/v1 This work is licensed under a CC BY 4.0 License Status: Published Journal Publication published 02 May, 2025 Read the published version in Scientific Reports → Version 1 posted 10 You are reading this latest preprint version Abstract Scraper conveyor load prediction is crucial to realize the cooperative speed regulation of coal mining machine and scraper conveyor. In the synthesized mining face, due to the uncertainty of the coal fall, the load of the scraper conveyor fluctuates due to the change of the coal load, which shows a strong nonlinearity and non-smoothness, leading to the difficulty of prediction. To solve this problem, this paper proposes a BP neural network model combined with wavelet transform for scraper conveyor current prediction. By studying the mapping relationship between motor load and current based on the BP neural network algorithm, and taking the scraper conveyor current as the input condition, wavelet decomposition and data reconstruction of historical current data are carried out, and time series prediction is performed on the original data samples and reconstructed data samples, respectively. The simulation results show that the reconstructed BP neural network model using wavelet decomposition has higher prediction accuracy, in which the root mean square error is reduced by 13.26%, the average absolute error is reduced by 14.19%, and the percentage error is reduced by 17.43%. The model meets the accuracy requirements of scraper conveyor load prediction, and can provide theoretical basis for cooperative speed regulation of coal mining machine and scraper conveyor. Physical sciences/Engineering/Mechanical engineering Physical sciences/Energy science and technology/Fossil fuels/Coal Time series prediction Wavelet transform Load prediction BP neural network Full Text Additional Declarations No competing interests reported. Supplementary Files rawdata.xlsx Cite Share Download PDF Status: Published Journal Publication published 02 May, 2025 Read the published version in Scientific Reports → Version 1 posted Editorial decision: Accepted 28 Apr, 2025 Reviews received at journal 26 Apr, 2025 Reviews received at journal 22 Apr, 2025 Reviews received at journal 18 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviewers agreed at journal 16 Apr, 2025 Reviewers invited by journal 16 Apr, 2025 Submission checks completed at journal 15 Apr, 2025 First submitted to journal 05 Apr, 2025 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. 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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-5440016","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Article","associatedPublications":[],"authors":[{"id":443865386,"identity":"ab1e3fe1-ce91-4757-bba3-9859a72dc8e6","order_by":0,"name":"Dan Zhang","email":"","orcid":"","institution":"Heilongjiang University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Dan","middleName":"","lastName":"Zhang","suffix":""},{"id":443865387,"identity":"4a6570a7-0373-475d-a79a-f5366192ac7b","order_by":1,"name":"Jiafeng Qin","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA1klEQVRIiWNgGAWjYBACfvnHxz98qPjPw8bffIA4LZINaWmMM84wy/FLHEsgTovBgRwzZt42ZmPJhhwDIl124IDZA54zbIkbDpz5eOMNg52cbgMBHYyNDekGEhU8iRsO9262nMOQbGx2gIAWZmaGAxIGZySAtpzdJs3DcCBxGyEtbGyMDRKJbQZALTnPiNPCw8PMJnGwLQHkfTbitEhIsDEbNpw5AApkY8s5BkT4xf4G/8fHfyoOgKLy4Y03FXZyBLWgWslDbNQgaSFVxygYBaNgFIwIAAB9YUZTSICpfAAAAABJRU5ErkJggg==","orcid":"","institution":"Heilongjiang University of Science and Technology","correspondingAuthor":true,"prefix":"","firstName":"Jiafeng","middleName":"","lastName":"Qin","suffix":""},{"id":443865388,"identity":"a98e61c9-5169-450e-8e32-7ea1c14ca770","order_by":2,"name":"Weidong Wu","email":"","orcid":"","institution":"Heilongjiang University of Science and Technology","correspondingAuthor":false,"prefix":"","firstName":"Weidong","middleName":"","lastName":"Wu","suffix":""},{"id":443865389,"identity":"c03ed84f-5d2a-4d79-bb13-729ef90fe0fa","order_by":3,"name":"Yongtao Zhu","email":"","orcid":"","institution":"Production Command Center of Shuangyashan Mining Group Co. 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In the synthesized mining face, due to the uncertainty of the coal fall, the load of the scraper conveyor fluctuates due to the change of the coal load, which shows a strong nonlinearity and non-smoothness, leading to the difficulty of prediction. To solve this problem, this paper proposes a BP neural network model combined with wavelet transform for scraper conveyor current prediction. By studying the mapping relationship between motor load and current based on the BP neural network algorithm, and taking the scraper conveyor current as the input condition, wavelet decomposition and data reconstruction of historical current data are carried out, and time series prediction is performed on the original data samples and reconstructed data samples, respectively. The simulation results show that the reconstructed BP neural network model using wavelet decomposition has higher prediction accuracy, in which the root mean square error is reduced by 13.26%, the average absolute error is reduced by 14.19%, and the percentage error is reduced by 17.43%. The model meets the accuracy requirements of scraper conveyor load prediction, and can provide theoretical basis for cooperative speed regulation of coal mining machine and scraper conveyor.\u003c/p\u003e","manuscriptTitle":"Research on scraper conveyor load prediction method based on wavelet transform and BP neural network","msid":"","msnumber":"","nonDraftVersions":[{"code":1,"date":"2025-04-17 12:17:00","doi":"10.21203/rs.3.rs-5440016/v1","editorialEvents":[{"type":"communityComments","content":0},{"type":"decision","content":"Accepted","date":"2025-04-28T06:50:50+00:00","index":"","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-27T01:11:39+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-22T08:26:19+00:00","index":"hide","fulltext":""},{"type":"editorInvitedReview","content":"","date":"2025-04-18T11:50:31+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"150474544836344539852251513442251059635","date":"2025-04-17T01:23:19+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"198101590641754214246697649407725218603","date":"2025-04-16T13:18:30+00:00","index":"hide","fulltext":""},{"type":"reviewerAgreed","content":"77443884445724465081227077682209789739","date":"2025-04-16T13:18:02+00:00","index":"hide","fulltext":""},{"type":"reviewersInvited","content":"","date":"2025-04-16T13:05:20+00:00","index":"","fulltext":""},{"type":"checksComplete","content":"","date":"2025-04-15T11:57:27+00:00","index":"","fulltext":""},{"type":"submitted","content":"Scientific Reports","date":"2025-04-05T04:58:45+00:00","index":"","fulltext":""}],"status":"published","journal":{"display":true,"email":"[email protected]","identity":"scientific-reports","isNatureJournal":false,"hasQc":true,"allowDirectSubmit":false,"externalIdentity":"scirep","sideBox":"Learn more about [Scientific Reports](http://www.nature.com/srep/)","snPcode":"","submissionUrl":"","title":"Scientific Reports","twitterHandle":"","acdcEnabled":true,"dfaEnabled":true,"editorialSystem":"stoa","reportingPortfolio":"Scientific Reports","inReviewEnabled":true,"inReviewRevisionsEnabled":true}}],"origin":"","ownerIdentity":"b6e52ea3-798d-4445-9c5e-38dcdfe03dd7","owner":[],"postedDate":"April 17th, 2025","published":true,"recentEditorialEvents":[],"rejectedJournal":[],"revision":"","amendment":"","status":"published-in-journal","subjectAreas":[{"id":47258000,"name":"Physical sciences/Engineering/Mechanical engineering"},{"id":47258001,"name":"Physical sciences/Energy science and technology/Fossil fuels/Coal"}],"tags":[],"updatedAt":"2025-05-05T16:06:49+00:00","versionOfRecord":{"articleIdentity":"rs-5440016","link":"https://doi.org/10.1038/s41598-025-00333-7","journal":{"identity":"scientific-reports","isVorOnly":false,"title":"Scientific Reports"},"publishedOn":"2025-05-02 15:56:54","publishedOnDateReadable":"May 2nd, 2025"},"versionCreatedAt":"2025-04-17 12:17:00","video":"","vorDoi":"10.1038/s41598-025-00333-7","vorDoiUrl":"https://doi.org/10.1038/s41598-025-00333-7","workflowStages":[]},"version":"v1","identity":"rs-5440016","journalConfig":"researchsquare"},"__N_SSP":true},"page":"/article/[identity]/[[...version]]","query":{"redirect":"/article/rs-5440016","identity":"rs-5440016","version":["v1"]},"buildId":"8U1c8b4HqxoKbykW_rLl7","isFallback":false,"isExperimentalCompile":false,"dynamicIds":[84888],"gssp":true,"scriptLoader":[]}