[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-117931-en":3,"doc-seo-117931-105":30,"detail-sidebar-cat-0-en-105":91},{"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":4,"is_deleted":4,"is_public":20,"is_downloadable":20,"audit_status":20,"page_count":21,"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},117931,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","Fault Detection Analysis in Ball Bearings using Machine Learning Techniques","Fault detection and diagnosis for rolling-element bearings are critical for maintaining the reliability of rotating machinery and reducing costly downtime. The work reviews and highlights machine-learning approaches for misalignment-related bearing problems, emphasizing how defects create periodic contacts and excitation that degrade efficiency or cause failure. It covers conventional methods such as ANN, decision trees, random forests, and SVM alongside growing deep-learning interest. It also outlines signal-based processing using vibration and phase current data for timely detection.","Fault Detection Analysis in Ball Bearings using Machine  \nLearning Techniques  \nChandan D. Chaudhari, Chinmayee Chogale, Dwij Iyer, Shubham Junghare, Vedant Surve  \nDepartment of Mechanical Engineering  \nSIES Graduate School of Technology  \nMumbai, India  \nArticle history:  \nReceived Jun 2, 2023 Revised Jun 18, 2023 Accepted July 24, 2023  \nKeywords:  \nMisalignment Fault Detection Bearing Machine Learning  \nCorresponding Author:  \nThe Bearing element is very essential component of any rotating equipment. Any defect in the bearings lead to instable performance of the machinery. To avoid such malfunction and breakdown of the machinery equipment due to misalignment is review critically in this research paper and various machine learning techniques to tackle the issue is highlighted. This review article finds the basis for developing an effective system in order to reduce the breakdown of machinery or equipment. Conventional Machine Learning methods, like Artificial neural network, Decision Tree, Random Forest, Support Vector Machines (SVM) have been applied to detecting categorizing fault, while the application of Deep Learning methods has ignited great interest in the industry.  \nThis is an open access article under the CC BY license.  \nShubham Junghare,  \nDepartment of Mechanical Engineering SIES Graduate School of Technology, Mumbai, India. [Email : shubhamtjunghare@gmail.com](Email : shubhamtjunghare@gmail.com)  \nArticle Info ABSTRACT  \n1. INTRODUCTION  \nIn the recent years, condition monitoring and fault diagnosis of equipment are of great concern in industries. Timely Fault analysis in machineries can save millions of dollars in maintenance cost. In rotating machineries, bearings are one of the most critical components because they are the most commonly wearing parts and a large majority of system failures arise from faulty bearings. Proper working of these elements is extremely important in industry in order to prevent long term costly downtimes. It is obvious that more attention must be paid to the condition of a rolling element bearing if the human life is in question. Thus, an advanced technology is needed to monitor the health status of bearings efficiently and effectively.  \nRoller bearings consists of different parts: an outer-race, an inner-race, roller-elements that are in contact under heavy dynamic loads and relatively high speeds, and optionally a cage around these rolling elements. Faults may occur in any of these parts, and often these faults are single point defects such as chips or dents. As these elements move past each other, these defects come into periodic contact with other elements in the bearing, and at each contact they can excite a high frequency resonance in the overall structure. Bearing damage may result in a complete failure of the bearing however, in a reduction in operating efficiency of the bearing arrangement. Only if operating and environmental conditions as well as the details of the bearing arrangement are completely in tune, can the bearing arrangement operate efficiently. In the recent years, manufacturers have been concentrating on finding out techniques in order to improve the bearing designs.  \nResearchers are using various approaches like mathematical models, computer aided engineering (CAE) based simulation models’ experimental models. The purpose of this paper is to give details of the different measurement techniques in the last period on bearing defects. Different methodologies for detection and diagnosis of bearing defects:  \n• Vibration measurements  \n• Acoustics measurement technique  \n• Temperature measurements  \n• Wear debris analysis  \nA significant portion of the papers on the fault diagnosis of induction machines are dealing with on the faults of rolling bearings. Even though that vibration-based condition monitoring techniques are usually applied for the diagnosis of the bearings, many papers use the stator current analysis, due to its advantages. The methods used for s","cbCaipiWTgfMnS4B","https://ap.wps.com/l/cbCaipiWTgfMnS4B","pdf",499826,1,9,"English","en",105,"# Introduction\n## Bearing faults and condition monitoring\n## Fault detection methodologies and measurements\n# Literature Review\n## Experimental studies on bearing damage","[{\"question\":\"Why are bearing faults important for rotating equipment reliability?\",\"answer\":\"Bearings are critical wearing components in rotating machinery, and faulty bearings can destabilize performance and lead to failures or reduced operating efficiency. Timely fault analysis helps prevent long-term downtime and high maintenance costs.\"},{\"question\":\"Which signal-based measurements are discussed for detecting bearing defects?\",\"answer\":\"The document discusses vibration measurements, acoustics, temperature measurements, and wear debris analysis. It also focuses on motor vibration and phase current measurements as signal-based methods for fault detection and diagnosis.\"},{\"question\":\"What machine learning approaches are highlighted for fault detection in bearings?\",\"answer\":\"Conventional machine learning methods include artificial neural networks, decision trees, random forests, and support vector machines. The document also notes increasing industrial interest in deep learning methods for fault diagnosis.\"}]","Fault Detection Analysis in Ball Bearings using Machine Learning Techniques | PDF",1785680424,23,{"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":86,"head_meta":88,"extra_data":90,"updated_unix":28},"fault-detection-analysis-in-ball-bearings-using-machine-learning-techniques","",{"@graph":36,"@context":85},[37,54,68],{"@type":38,"itemListElement":39},"BreadcrumbList",[40,44,48,51],{"item":41,"name":42,"@type":43,"position":20},"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/fault-detection-analysis-in-ball-bearings-using-machine-learning-techniques/117931/",4,{"url":52,"name":13,"@type":55,"author":56,"headline":13,"publisher":58,"fileFormat":61,"inLanguage":23,"description":14,"dateModified":62,"datePublished":62,"encodingFormat":61,"isAccessibleForFree":63,"interactionStatistic":64},"DigitalDocument",{"name":9,"@type":57},"Person",{"url":41,"name":59,"@type":60},"DocShare","Organization","application/pdf","2026-08-02",true,{"@type":65,"interactionType":66,"userInteractionCount":4},"InteractionCounter",{"@type":67},"ViewAction",{"@type":69,"mainEntity":70},"FAQPage",[71,77,81],{"name":72,"@type":73,"acceptedAnswer":74},"Why are bearing faults important for rotating equipment reliability?","Question",{"text":75,"@type":76},"Bearings are critical wearing components in rotating machinery, and faulty bearings can destabilize performance and lead to failures or reduced operating efficiency. Timely fault analysis helps prevent long-term downtime and high maintenance costs.","Answer",{"name":78,"@type":73,"acceptedAnswer":79},"Which signal-based measurements are discussed for detecting bearing defects?",{"text":80,"@type":76},"The document discusses vibration measurements, acoustics, temperature measurements, and wear debris analysis. It also focuses on motor vibration and phase current measurements as signal-based methods for fault detection and diagnosis.",{"name":82,"@type":73,"acceptedAnswer":83},"What machine learning approaches are highlighted for fault detection in bearings?",{"text":84,"@type":76},"Conventional machine learning methods include artificial neural networks, decision trees, random forests, and support vector machines. 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