[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-123729-en":3,"doc-seo-123729-105":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},123729,16904993612988,"Olivia Brown","https://ap-avatar.wpscdn.com/davatar_a8503ba1806abce46bf441b54a3ca4cd",8,"Research & Report","Vibration-Based Machine Learning Models for Condition Monitoring of Railroad Rolling Stock","One of the primary causes of rail rolling stock derailments is linked to bearing and wheel axle failures. Train bearing health is commonly monitored at specific target locations using wayside detection systems, leaving gaps between sensors where failures and potential derailments can occur. A wireless onboard monitoring system developed by UTCRS continuously captures vibration responses correlated with bearing health. Regression-based machine learning and long-term prediction neural networks are trained to forecast bearing condition and support two-way validation with onboard sensors.","University of Texas Rio Grande Valley  \nScholarWorks @ UTRGV  \nTheses and Dissertations-UTRGV  \n8-2023  \nVibration-Based Machine Learning Models for Condition Monitoring of Railroad Rolling Stock  \nSergio M. Martinez  \nThe University of Texas Rio Grande Valley  \nFollow this and additional works at: [https://scholarworks.utrgv.edu/etd](https://scholarworks.utrgv.edu/etd)  \n Part of the Mechanical Engineering Commons  \nRecommended Citation  \nMartinez, Sergio M., \"Vibration-Based Machine Learning Models for Condition Monitoring of Railroad Rolling Stock\" (2023) . Theses and Dissertations-UTRGV. 1368.  \n[https://scholarworks.utrgv.edu/etd/1368](https://scholarworks.utrgv.edu/etd/1368)  \nThis Thesis is brought to you for free and open access by ScholarWorks @ UTRGV. It has been accepted for inclusion in Theses and Dissertations-UTRGV by an authorized administrator of ScholarWorks @ UTRGV. For more information, [please contact justin.white@utrgv.edu](please contact justin.white@utrgv.edu), [william.flores01@utrgv.edu](william.flores01@utrgv.edu).  \nVIBRATION-BASED MACHINE LEARNING MODELS FOR CONDITION MONITORING OF RAILROAD ROLLING STOCK  \nA Thesis  \nby  \nSERGIO M. MARTINEZ  \nSubmitted in Partial Fulfillment of the Requirements for the Degree of  \nMASTER OF SCIENCE IN ENGINEERING  \nMajor Subject: Mechanical Engineering  \nThe University of Texas Rio Grande Valley August 2023  \nVIBRATION-BASED MACHINE LEARNING MODELS FOR CONDITION MONITORING OF RAILROAD ROLLING STOCK  \nA Thesis  \nby  \nSERGIO M. MARTINEZ  \nCOMMITTEE MEMBERS  \nDr. Constantine Tarawneh  \nCo-Chair of Committee  \nDr. Mohamadhossein Noruzoliaee Co-Chair of Committee  \nDr. Fatemeh Nazari  \nCommittee Member  \nDr. Heinrich Foltz  \nCommittee Member  \nAugust 2023  \nCopyright 2023 Sergio M. Martinez All Rights Reserved  \nABSTRACT  \nMartinez, Sergio M., Vibration-Based Machine Learning Models for Condition Monitoring of Railroad Rolling Stock. Master of Science in Engineering (MSE), August, 2023, 78 pp., 13 tables, 29 figures, references, 26 titles.  \nOne of the primary causes of rail rolling stock derailments is attributed to bearing and wheel axle failures. The health of train bearings is primarily monitored at target locations through wayside detection systems. This practice is susceptible to bearing failure and potential derailments at points in between these wayside systems. To remedy this, the University Transportation Center for Railway Safety (UTCRS) has developed a wireless onboard monitoring system that can continuously monitor the vibration response, which directly correlates to the health of bearings. This data is used to train regression-based machine learning algorithms and long-term prediction neural networks to predict bearing health. The models are intended to work in tandem with the onboard monitoring sensors as a means of two-way practical validation. The models tested were the Gradient Boosting Machine architecture for scheduled predictions and the Informer neural network architecture for long-term predictions of ongoing routes. The dataset for these models comes from the expansive laboratory record data available at the UTCRS. Ultimately, these machine learning algorithms will enhance freight railcars safety and save rail companies money by allowing for predictive maintenance practices.  \nDEDICATION  \nThis thesis is dedicated to my family, friends, and everyone who supported me in obtaining such a milestone. The first person I would like to thank is my mother, Amalia G. Martinez. I would not have been able to accomplish all that I have without the unending support you have given me. To my father, Sergio M. Martinez Sr., I am grateful for the work you put into supporting our family and always supporting me in pursuing higher education.  \nI thank my sisters, Amy D. Martinez, Sydney M. Martinez, and Arianna D. Martinez, for our fond memories and laughs. To Amy, I am grateful for you being there for me, from helping with my schoolwork to introducing me to many hob","cbCaicDaYhpQnbWr","https://ap.wps.com/l/cbCaicDaYhpQnbWr","pdf",2868530,1,99,"English","en",105,"# Abstract\n## Background and problem statement\n## Wireless onboard vibration monitoring\n## Machine learning and long-term prediction models\n## Dataset and validation intent","[{\"question\":\"What failures motivate the thesis work on railroad monitoring?\",\"answer\":\"Rail derailments are primarily attributed to bearing and wheel axle failures, with a focus on how bearing problems may develop between traditional wayside detection points.\"},{\"question\":\"How does the wireless onboard monitoring system contribute?\",\"answer\":\"It continuously measures vibration responses on board, and those vibrations are used as signals that correlate with bearing health.\"},{\"question\":\"Which machine learning approaches are tested for different prediction horizons?\",\"answer\":\"The thesis tests a Gradient Boosting Machine architecture for scheduled predictions and an Informer neural network architecture for long-term predictions on ongoing routes.\"}]","Vibration-Based Machine Learning Models for Condition Monitoring of Railroad Rolling Stock | 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