Advanced Fault Detection in Hydraulic Actuator Cylinders of Heavy Machinery Using Neural Networks

Advanced Fault Detection in Hydraulic Actuator Cylinders of Heavy Machinery Using Neural Networks | 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 Advanced Fault Detection in Hydraulic Actuator Cylinders of Heavy Machinery Using Neural Networks Gyan Wrat, Prabhat Ranjan, Santosh Kr. Mishra, Joseph T. Jose, and 1 more This is a preprint; it has not been peer reviewed by a journal. https://doi.org/ 10.21203/rs.3.rs-4576511/v1 This work is licensed under a CC BY 4.0 License Status: Under Revision Version 1 posted 5 You are reading this latest preprint version Abstract This work presents for detecting internal leakage faults in hydraulic actuator cylinders using signal analysis and a supervised artificial neural network classifier. An artificial leakage is introduced and a signal-based fault detection method is employed to process and transform the signals for internal leakage detection. The analysis focuses on extracting features from the pressure signal, particularly the peaks, which include information as location, height, and width. Once the neural network is trained, it is utilized to classify the fault level into three categories: healthy system, system with low fault, and system with high fault. The proposed technique utilizes pressure signals and extracts features from the peak signals to reduce dimensionality. This method offers advantages such as reduced computational cost through feature extraction and dimensionality reduction, and it is capable of detecting multiple leakage classes. This effective technique based on artificial neural networks for detecting internal leakage faults in hydraulic cylinders. internal leakage fault signal analysis supervised artificial neural network classifier fault detection method feature extraction dimensionality reduction Full Text Cite Share Download PDF Status: Under Revision Version 1 posted Editorial decision: Major Revisions Needed 01 Sep, 2024 Reviewers agreed at journal 28 Jun, 2024 Reviewers invited by journal 21 Jun, 2024 Editor assigned by journal 17 Jun, 2024 First submitted to journal 14 Jun, 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. 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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-4576511","acceptedTermsAndConditions":true,"allowDirectSubmit":false,"archivedVersions":[],"articleType":"Research Article","associatedPublications":[],"authors":[{"id":317412073,"identity":"90b3021a-947a-48c0-88fb-b38046d6979e","order_by":0,"name":"Gyan Wrat","email":"data:image/png;base64,iVBORw0KGgoAAAANSUhEUgAAAZAAAAAyAQMAAABI0h/eAAAABlBMVEX///8AAABVwtN+AAAACXBIWXMAAA7EAAAOxAGVKw4bAAAA0UlEQVRIiWNgGAWjYLCCDyi8M0ToYJxBshZmHpK0yEfkmH22qbgjZ96/xoDhR81hBr4zB/BrMbyRYzw758wzY5kbbwwYe44dZpA820BAy4zczcy5bYcTZ0icMWDgYTvMYHCegMPAWiz/Ha4HaWH8848ILfISQC2MDYcTJPh7DJh524BaCDnMgOf9Z5AXDGdIsBUclu1L55Ek5H359rRkUEDJS/Af3vjwzTdrOb4zCQRsuQBTIJFhADKeB7damC39MGfwH39AUPUoGAWjYBSMTAAA/VBIGTIvjJsAAAAASUVORK5CYII=","orcid":"https://orcid.org/0000-0002-6351-4450","institution":"Aalborg University: Aalborg Universitet","correspondingAuthor":true,"prefix":"","firstName":"Gyan","middleName":"","lastName":"Wrat","suffix":""},{"id":317412074,"identity":"7cc07b4d-204c-432b-97c2-9b4a705c4e30","order_by":1,"name":"Prabhat Ranjan","email":"","orcid":"","institution":"","correspondingAuthor":false,"prefix":"","firstName":"Prabhat","middleName":"","lastName":"Ranjan","suffix":""},{"id":317412075,"identity":"e1bb2a28-eb00-48f0-885e-2f732c688614","order_by":2,"name":"Santosh Kr. 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