[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119326-en":3,"doc-seo-119326-105":29,"detail-sidebar-cat-0-en-105":90},{"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":20,"is_downloadable":20,"audit_status":20,"page_count":11,"language":21,"language_code":22,"site_id":23,"html_lang":22,"table_of_contents":24,"faqs":25,"seo_title":26,"seo_description":14,"update_tm":27,"read_time":28},119326,1374391975076,"Riley","https://ap-avatar.wpscdn.com/avatar/14000253ca4ec9f6853?x-image-process=image/resize,m_fixed,w_180,h_180&k=1783305029341752051",8,"Research & Report","Empirical Study of Machine Learning for Intelligent Bearing Fault Diagnosis","Bearing failures significantly degrade manufacturing system performance and reliability, while also creating safety risks and potential losses of life and property. Research shows bearing faults account for roughly 30–40% of failures in induction motors, making early fault identification essential for uninterrupted operation. Faults in inner race, outer race, ball, and cage components alter vibration signals, enabling detection by comparing normal and faulty patterns. Using a public benchmark dataset, the study evaluates how data preparation (dimension reduction and noise removal) and feature extraction affect machine-learning-based fault detection and classification, and discusses implications for early diagnosis and more efficient maintenance planning.","Santa Clara University  \nScholar Commons  \n\n| General Engineering | School of Engineering |\n| --- | --- |\n| 7-2023\u003Cbr>Empirical Study of Machine Learning for Intelligent Bearing Fault Diagnosis\u003Cbr>Armin Moghadam\u003Cbr>Fatemeh Davoudi Kakhki\u003Cbr>Santa Clara University, [fdavoudikakhki@scu.edu](fdavoudikakhki@scu.edu)\u003Cbr>Follow this and additional works at: [https://scholarcommons.scu.edu/eng_grad](https://scholarcommons.scu.edu/eng_grad) |  |\n\nRecommended Citation  \nMoghadam, A., & Kakhki, F. D. (2023) . Empirical Study of Machine Learning for Intelligent Bearing Fault Diagnosis. Production Management and Process Control, 104(104) . [https://doi.org/10.54941/](https://doi.org/10.54941/)[ ](https://doi.org/10.54941/)ahfe1003049  \nThis Conference Proceeding is brought to you for free and open access by the School of Engineering at Scholar Commons. It has been accepted for inclusion in General Engineering by an authorized administrator of Scholar Commons. For more information, please contact [rscroggin@scu.edu](rscroggin@scu.edu).  \nEmpirical Study of Machine Learning for Intelligent Bearing Fault Diagnosis  \nArmin Moghadam 1 and Fatemeh Davoudi Kakhki 1,2  \n1 Department of Technology, San Jose State University, San Jose, CA 95192, USA  \n2 Machine Learning & Safety Analytics Lab, Department of Technology, San Jose State University, San Jose, CA 95192, USA  \nABSTRACT  \nBearing failure highly impacts performance and production of manufacturing systems, causes safety incidents, and results in casualties and property loss. According to the current literature, bearing faults cause 30-40% of all failures in induction motors. Therefore, identification of bearing faults, at early stages, is crucial to ensure seamless and reliable operation of induction motors in industrial and manufacturing operations. Faults occur in four components of bearing: inner race, outer race, ball, and cage. Regardless of the component in which fault occurs, it causes changes in vibration signals. Therefore, comparing normal signals with faulty ones is helpful in detecting localized faults in bearings. We use a benchmark publicly-available data set to conduct this analysis. The main challenge in using publicly-available benchmark datasets for fault detection is lack of manual for instruction on analysis experiments on the original data, which leaves researchers with the challenge and opportunity of applying various analytical methods for achieving higher accuracy rates and useful models for fault detection. This study presents a machine learning-based fault detection and classification scheme in induction motors to evaluate the significance and effects of various data preparation and feature extraction methods on accuracy and reliability of fault detection outcomes. The data preparation stage includes discussion of efficient data dimension reduction, and noise eradication, as well as feature extraction methods for induction motor signals. The main methodology is developing a variety of machine learning classifiers for detection and classification of normal bearings versus faulty bearings. Finally, the implications of the methodology and results for early fault diagnosis and enhanced reliability, as well as maintenance planning efforts in manufacturing systems are discussed. This study introduces proper implementation of machine learning models to improve system performance with higher speed and reliability. Furthermore, the methodology and results contribute to planning and undertaking maintenance operation more efficiently. Therefore, the approach, methodology, and results will be beneficial to both researchers and practitioners involved in manufacturing systems reliability analysis and optimization.  \nKeywords: Machine learning, Monitoring and fault diagnosis, Bearing faults  \nINTRODUCTION  \nCondition monitoring of industrial systems is significant in enhancing safety, reliability, performance, and overall quality of industrial processes through providing insights on diag","cbCaievoaFDrOB0U","https://ap.wps.com/l/cbCaievoaFDrOB0U","pdf",756859,1,"English","en",105,"# Abstract\n# Introduction\n## Condition monitoring and safety relevance\n## Model-based vs data-driven approaches\n## Role of publicly available datasets\n# Methodology Overview\n## Data preparation and noise eradication\n## Feature extraction for induction motor signals\n## Machine learning classifiers for fault detection and classification\n# Implications and Conclusions\n## Early fault diagnosis and reliability\n## Maintenance planning in manufacturing systems","[{\"question\":\"Why is early bearing fault diagnosis important for induction motors?\",\"answer\":\"Bearing faults strongly impact performance and can lead to safety incidents and losses. Since bearing faults account for about 30–40% of failures in induction motors, early identification supports seamless and reliable operation.\"},{\"question\":\"How do bearing faults affect the signals used for diagnosis?\",\"answer\":\"Faults in the inner race, outer race, ball, and cage cause changes in vibration signals. Comparing normal signals with faulty ones helps detect localized faults.\"},{\"question\":\"What factors does the study evaluate to improve fault detection accuracy?\",\"answer\":\"The study investigates the effect of data preparation methods (dimension reduction and noise eradication) and feature extraction approaches, then applies various machine-learning classifiers to detect and classify normal versus faulty bearings.\"}]","Empirical Study of Machine Learning for Intelligent Bearing Fault Diagnosis | PDF",1785723725,20,{"code":4,"msg":30,"data":31},"ok",{"site_id":23,"language":22,"slug":32,"title":13,"keywords":33,"description":14,"schema_data":34,"social_meta":85,"head_meta":87,"extra_data":89,"updated_unix":27},"empirical-study-of-machine-learning-for-intelligent-bearing-fault-diagnosis","",{"@graph":35,"@context":84},[36,53,67],{"@type":37,"itemListElement":38},"BreadcrumbList",[39,43,47,50],{"item":40,"name":41,"@type":42,"position":20},"https://docshare.wps.com","Home","ListItem",{"item":44,"name":45,"@type":42,"position":46},"https://docshare.wps.com/document/","Document",2,{"item":48,"name":12,"@type":42,"position":49},"https://docshare.wps.com/document/research-report/",3,{"item":51,"name":13,"@type":42,"position":52},"https://docshare.wps.com/document/empirical-study-of-machine-learning-for-intelligent-bearing-fault-diagnosis/119326/",4,{"url":51,"name":13,"@type":54,"author":55,"headline":13,"publisher":57,"fileFormat":60,"inLanguage":22,"description":14,"dateModified":61,"datePublished":61,"encodingFormat":60,"isAccessibleForFree":62,"interactionStatistic":63},"DigitalDocument",{"name":9,"@type":56},"Person",{"url":40,"name":58,"@type":59},"DocShare","Organization","application/pdf","2026-08-03",true,{"@type":64,"interactionType":65,"userInteractionCount":20},"InteractionCounter",{"@type":66},"ViewAction",{"@type":68,"mainEntity":69},"FAQPage",[70,76,80],{"name":71,"@type":72,"acceptedAnswer":73},"Why is early bearing fault diagnosis important for induction motors?","Question",{"text":74,"@type":75},"Bearing faults strongly impact performance and can lead to safety incidents and losses. Since bearing faults account for about 30–40% of failures in induction motors, early identification supports seamless and reliable operation.","Answer",{"name":77,"@type":72,"acceptedAnswer":78},"How do bearing faults affect the signals used for diagnosis?",{"text":79,"@type":75},"Faults in the inner race, outer race, ball, and cage cause changes in vibration signals. Comparing normal signals with faulty ones helps detect localized faults.",{"name":81,"@type":72,"acceptedAnswer":82},"What factors does the study evaluate to improve fault detection accuracy?",{"text":83,"@type":75},"The study investigates the effect of data preparation methods (dimension reduction and noise eradication) and feature extraction approaches, then applies various machine-learning classifiers to detect and classify normal versus faulty bearings.","https://schema.org",{"og:url":51,"og:type":86,"og:title":13,"og:site_name":58,"og:description":14},"article",{"robots":88,"canonical":51},"index,follow",{"doc_id":7,"site_id":23},{"code":4,"msg":5,"data":91},[92,96,100,104,109,114,119,122,126,129,133],{"id":20,"doc_module":4,"doc_module_name":45,"category_name":93,"show_sort_weight":94,"slug":95},"Story & Novel",90,"story-novel",{"id":46,"doc_module":4,"doc_module_name":45,"category_name":97,"show_sort_weight":98,"slug":99},"Literature",80,"literature",{"id":52,"doc_module":4,"doc_module_name":45,"category_name":101,"show_sort_weight":102,"slug":103},"Exam",70,"exam",{"id":105,"doc_module":4,"doc_module_name":45,"category_name":106,"show_sort_weight":107,"slug":108},5,"Comic",60,"comic",{"id":110,"doc_module":4,"doc_module_name":45,"category_name":111,"show_sort_weight":112,"slug":113},6,"Technology",50,"technology",{"id":115,"doc_module":4,"doc_module_name":45,"category_name":116,"show_sort_weight":117,"slug":118},7,"Healthcare",40,"healthcare",{"id":11,"doc_module":4,"doc_module_name":45,"category_name":12,"show_sort_weight":120,"slug":121},30,"research-report",{"id":123,"doc_module":4,"doc_module_name":45,"category_name":124,"show_sort_weight":28,"slug":125},9,"Religion & Spirituality","religion-spirituality",{"id":28,"doc_module":4,"doc_module_name":45,"category_name":127,"show_sort_weight":28,"slug":128},"World Cup","world-cup",{"id":130,"doc_module":4,"doc_module_name":45,"category_name":131,"show_sort_weight":130,"slug":132},10,"Lifestyle","lifestyle",{"id":134,"doc_module":4,"doc_module_name":45,"category_name":135,"show_sort_weight":105,"slug":136},19,"General","general"]