[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-126265-en":3,"doc-seo-126265-105":30,"detail-sidebar-cat-0-en-105":92},{"code":4,"msg":5,"data":6},0,"success",{"doc_id":7,"user_id":8,"nickname":9,"user_avatar":10,"doc_module":4,"category_id":11,"category_name":12,"doc_title":13,"doc_description":14,"doc_content":15,"file_id":16,"file_url":17,"file_type":18,"file_size":19,"view_count":20,"is_deleted":4,"is_public":21,"is_downloadable":21,"audit_status":21,"page_count":11,"language":22,"language_code":23,"site_id":24,"html_lang":23,"table_of_contents":25,"faqs":26,"seo_title":27,"seo_description":14,"update_tm":28,"read_time":29},126265,2336475104362,"Eden","https://ap-avatar.wpscdn.com/avatar/22000c4c46a41b752dd?x-image-process=image/resize,m_fixed,w_180,h_180&k=1786595829695023868",8,"Research & Report","Creation of a Machine Learning-Based Automated System for the Multi-Component Fault Analysis of Industrial Machines","The efficiency and reliability of industrial machines are paramount to keep manufacturing operations stable and reduce downtime. Complex multi-component systems make timely fault identification and diagnosis difficult, while manual inspection and simplistic rule-based approaches are slow, subjective, and error-prone. A machine learning-driven automated system is proposed to process large-scale sensor data, detect anomalies, and localize potential faults across multiple components simultaneously. The approach includes noisy-sensor data preprocessing, feature extraction, fault detection and classification, and a user-focused interface with explainability to improve trust in diagnostic outputs.","1Dr. Mohammad Ahmar Khan  \n2Chandradeep Bhatt  \n3Indrajeet Kumar  \n4Y. Rajesh Babu  \n5Ashish Kumar  \nKaushal  \n6Dr. Sarfraz Fayaz Khan  \nCreation of a Machine Learning-Based Automated System for the MultiComponent Fault Analysis of Industrial Machines  \nAbstract: -The efficiency and reliability of industrial machines are paramount for ensuring smooth operations and minimizing downtime in manufacturing environments. However, the complexity of these machines and the multitude of components they consist of pose significant challenges in identifying and diagnosing faults promptly. Traditional fault analysis methods often rely on manual inspection or simplistic rule-based systems, which are time-consuming, subjective, and prone to errors. In this study, we propose the development of a novel machine learning-based automated system for the multi-component fault analysis of industrial machines. Leveraging advancements in artificial intelligence and data analytics, our system aims to revolutionize fault detection and diagnosis by efficiently processing vast amounts of sensor data to identify anomalies and pinpoint potential faults across multiple components simultaneously. The proposed system comprises several key components, including data preprocessing techniques to handle noisy sensor data and extract relevant features, machine learning algorithms for fault detection and classification, and a user-friendly interface for visualization and interpretation of results. Additionally, the system will incorporate techniques for model explain ability to enhance trust and understanding of the automated diagnostic process.  \nKeywords: Industrial machines, Fault analysis, Machine learning, Automated system, Multi-component faults, Artificial intelligence, Data analytics  \nIntroduction  \nVirtually every industrial sector makes extensive use of rotating equipment. Machines that spin include things like fans, pumps, compressors, turbines, motors, generators, and so on. Rotating machine components are prone to failure due to continuous operations and different cyclic loading situations, which might result in catastrophic failure. Damages that may occur in rotating machinery include rotor imbalance, shaft misalignment, shaft looseness, bearing inner race fault and outer race fault, fractured gear teeth, and many more. In order to save  \n1 Dept. of MIS, CCBA, Dhofar University, Salalah, Sultanate of Oman\"  \n2Assistant Professor, Computer Science and Engineering, Graphic Era Hill University, Dehradun; Adjunct Professor, Graphic Era Deemed tobe University, Dehradun, Uttarakhand-248002, India.  \nMail [id-cbhatt@gehu.ac.in](id-cbhatt@gehu.ac.in)  \n3Associate Professor, Computer Science and Engineering, Graphic Era Hill University, Dehradun; Adjunct Professor, Graphic Era Deemed tobe University, Dehradun, Uttarakhand-248002, India  \nMail [id- ikumar@gehu.ac.in](id- ikumar@gehu.ac.in)  \n4Assistant Professor, Department of Computer Science and Engineering, Koneru Lakshmaiah Education Foundation, Vaddeswaram, Andhra Pradesh, India , [522302 yrajeshbabu7@gmail.com](522302 yrajeshbabu7@gmail.com)  \n5O P Jindal Global University, Sonepat, Haryana, India 131001 [ashishkiitd89@gmail.com](ashishkiitd89@gmail.com)  \n6School of Advance Technology, Algonquin College, Ottawa, Canada\"Copyright © JES [2024 on-line : journal.esrgroups.org](2024 on-line : journal.esrgroups.org)  \nmaintenance costs, prevent machine breakdowns, and maximize production, early problem detection is essential. In current industrial age, it is crucial to monitor machine conditions and identify problematic components in a timely manner for maintenance decision making. Predictive maintenance and condition-based maintenance (CBM) are terms that describe this approach to building upkeep. This tactic has been a game-changer in the realm of maintenance technology in the last few years.  \nFault Diagnosis  \nAn important part of condition-based maintenance is fault diagnosis, which entails collecting dat","cbCaivjYy2LAX2CR","https://ap.wps.com/l/cbCaivjYy2LAX2CR","pdf",238986,6,1,"English","en",105,"# Introduction\n## Fault Diagnosis\n## Vibration Monitoring and Signal Analysis\n## Frequency-Domain and FFT Processing\n## Limitations of Spectrum Analysis\n## Machine-Specific Vibration Methods","[{\"question\":\"Why is multi-component fault diagnosis challenging for industrial machines?\",\"answer\":\"Industrial machines contain many components and their operating complexity makes it hard to identify faults quickly and accurately. Traditional methods are also time-consuming and prone to subjectivity and error.\"},{\"question\":\"What sensors and data are commonly used in condition-based fault diagnosis?\",\"answer\":\"Fault diagnosis relies on collected variables and sensor signals, including vibration, acoustic emission, oil analysis, wear debris, infrared thermography, sound, and ultrasonic monitoring.\"},{\"question\":\"How does the proposed system support fault detection and diagnosis?\",\"answer\":\"It preprocesses noisy sensor data, extracts relevant features, applies machine learning for fault detection and classification, and provides a visualization interface with model explainability to improve understanding and trust.\"}]","Creation of a Machine Learning-Based Automated System for the Multi-Component Fault Analysis of Industrial Machines | PDF",1785904136,20,{"code":4,"msg":31,"data":32},"ok",{"site_id":24,"language":23,"slug":33,"title":13,"keywords":34,"description":14,"schema_data":35,"social_meta":87,"head_meta":89,"extra_data":91,"updated_unix":28},"creation-of-a-machine-learning-based-automated-system-for-the-multi-component-fault-analysis-of-industrial-machines","",{"@graph":36,"@context":86},[37,54,69],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":21},"https://docshare.wps.com","Home","ListItem",{"item":45,"name":46,"@type":43,"position":47},"https://docshare.wps.com/document/","Document",2,{"item":49,"name":12,"@type":43,"position":50},"https://docshare.wps.com/document/research-report/",3,{"item":52,"name":13,"@type":43,"position":53},"https://docshare.wps.com/document/creation-of-a-machine-learning-based-automated-system-for-the-multi-component-fault-analysis-of-industrial-machines/126265/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":63,"encodingFormat":61,"isAccessibleForFree":64,"interactionStatistic":65},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-23","2026-08-05",true,{"@type":66,"interactionType":67,"userInteractionCount":20},"InteractionCounter",{"@type":68},"ViewAction",{"@type":70,"mainEntity":71},"FAQPage",[72,78,82],{"name":73,"@type":74,"acceptedAnswer":75},"Why is multi-component fault diagnosis challenging for industrial machines?","Question",{"text":76,"@type":77},"Industrial machines contain many components and their operating complexity makes it hard to identify faults quickly and accurately. Traditional methods are also time-consuming and prone to subjectivity and error.","Answer",{"name":79,"@type":74,"acceptedAnswer":80},"What sensors and data are commonly used in condition-based fault diagnosis?",{"text":81,"@type":77},"Fault diagnosis relies on collected variables and sensor signals, including vibration, acoustic emission, oil analysis, wear debris, infrared thermography, sound, and ultrasonic monitoring.",{"name":83,"@type":74,"acceptedAnswer":84},"How does the proposed system support fault detection and diagnosis?",{"text":85,"@type":77},"It preprocesses noisy sensor data, extracts relevant features, applies machine learning for fault detection and classification, and provides a visualization interface with model explainability to improve understanding and trust.","https://schema.org",{"og:url":52,"og:type":88,"og:title":13,"og:site_name":59,"og:description":14},"article",{"robots":90,"canonical":52},"index,follow",{"doc_id":7,"site_id":24},{"code":4,"msg":5,"data":93},[94,98,102,106,111,115,120,123,127,130,134],{"id":21,"doc_module":4,"doc_module_name":46,"category_name":95,"show_sort_weight":96,"slug":97},"Story & Novel",90,"story-novel",{"id":47,"doc_module":4,"doc_module_name":46,"category_name":99,"show_sort_weight":100,"slug":101},"Literature",80,"literature",{"id":53,"doc_module":4,"doc_module_name":46,"category_name":103,"show_sort_weight":104,"slug":105},"Exam",70,"exam",{"id":107,"doc_module":4,"doc_module_name":46,"category_name":108,"show_sort_weight":109,"slug":110},5,"Comic",60,"comic",{"id":20,"doc_module":4,"doc_module_name":46,"category_name":112,"show_sort_weight":113,"slug":114},"Technology",50,"technology",{"id":116,"doc_module":4,"doc_module_name":46,"category_name":117,"show_sort_weight":118,"slug":119},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":46,"category_name":12,"show_sort_weight":121,"slug":122},30,"research-report",{"id":124,"doc_module":4,"doc_module_name":46,"category_name":125,"show_sort_weight":29,"slug":126},9,"Religion & Spirituality","religion-spirituality",{"id":29,"doc_module":4,"doc_module_name":46,"category_name":128,"show_sort_weight":29,"slug":129},"World Cup","world-cup",{"id":131,"doc_module":4,"doc_module_name":46,"category_name":132,"show_sort_weight":131,"slug":133},10,"Lifestyle","lifestyle",{"id":135,"doc_module":4,"doc_module_name":46,"category_name":136,"show_sort_weight":107,"slug":137},19,"General","general"]