[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-119868-en":3,"doc-seo-119868-105":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},119868,1649267921044,"Ava Thompson","https://us-avatar.wpscdn.com/avatar/1800007509477c92dfb?_k=1782875107921204101",8,"Research & Report","EARLY-WARNING PREDICTION FOR MACHINE FAILURES IN AUTOMATED INDUSTRIES USING ADVANCED MACHINE LEARNING TECHNIQUES","This Culminating Experience Project investigates the use of machine learning algorithms for machine failure detection and early-warning in industrial environments. It examines how input data quality, including outliers and noise, affects model accuracy and reliability. It evaluates how integrating SMOTE with feature engineering changes overall predictive performance. Results show that outlier handling and data balancing are essential, yet failure prediction remains challenging due to strong class imbalance; overall accuracy reaches 94% while failure true positives are limited.","California State University, San Bernardino  \nCSUSB ScholarWorks  \n\n| Electronic Theses, Projects, and Dissertations | Office of Graduate Studies |\n| --- | --- |\n| 12-2023\u003Cbr>EARLY-WARNING PREDICTION FOR MACHINE FAILURES IN AUTOMATED INDUSTRIES USING ADVANCED MACHINE LEARNING TECHNIQUES\u003Cbr>Satnam Singh\u003Cbr>Follow this and additional works at: [https://scholarworks.lib.csusb.edu/etd](https://scholarworks.lib.csusb.edu/etd)\u003Cbr> Part of the Business Analytics Commons, Business Intelligence Commons, Other Computer Engineering Commons, and the Technology and Innovation Commons |  |\n\nRecommended Citation  \nSingh, Satnam, \"EARLY-WARNING PREDICTION FOR MACHINE FAILURES IN AUTOMATED INDUSTRIES USING ADVANCED MACHINE LEARNING TECHNIQUES\" (2023) . Electronic Theses, Projects, and Dissertations. 1812.  \n[https://scholarworks.lib.csusb.edu/etd/1812](https://scholarworks.lib.csusb.edu/etd/1812)  \nThis Project is brought to you for free and open access by the Office of Graduate Studies at CSUSB ScholarWorks. It has been accepted for inclusion in Electronic Theses, Projects, and Dissertations by an authorized administrator of CSUSB ScholarWorks. For more information, please contact [scholarworks@csusb.edu](scholarworks@csusb.edu).  \nEARLY-WARNING PREDICTION FOR MACHINE FAILURES IN AUTOMATED  \nINDUSTRIES USING ADVANCED MACHINE LEARNING TECHNIQUES  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nIn Partial Fulfillment of the Requirements for the Degree Master of Science in  \nInformation Systems and Technology  \nby Satnam Singh December 2023  \nEARLY-WARNING PREDICTION FOR MACHINE FAILURES IN AUTOMATED  \nINDUSTRIES USING ADVANCED MACHINE LEARNING TECHNIQUES  \nA Project Presented to the Faculty of  \nCalifornia State University, San Bernardino  \nby  \nSatnam Singh  \nDecember 2023  \nApproved by:  \nDr. Conrad Shayo, Committee Member, Chair  \nDr. William Butler, Committee Member, Reader  \nDr. Conrad Shayo, Department Chair, Information and Decision Science  \n© 2023 Satnam Singh  \nABSTRACT  \nThis Culminating Experience Project explores the use of machine learning algorithms to detect machine failure. The research questions are: Q1) How does the quality of input data, including issues such as outliers, and noise, impact the accuracy and reliability of machine failure prediction models in industrial settings? Q2) How does the integration of SMOTE with feature engineering techniques influence the overall performance of machine learning models in detecting and preventing machine failures? Q3) What is the performance of different machine learning algorithms in predicting machine failures, and which algorithm is the most effective? The research findings are: Q1) Effective outlier handling is vital for predictive maintenance as the variables distribution initially showed a right-skewed pattern but after rectifying, it became more centralized, with correlations between specific sensors showing potential for further exploration. Q2) Data balancing through SMOTE and feature engineering is essential due to the rarity of actual failure instances. Substantial challenges are observed when predicting 'Failure' instances, with a lower true positive rate (73%), resulting in low precision (0 .02) and recall (0 .73) for 'Failure' predictions. This is further reflected in the low F1-Score (0 .03) for 'Failure,' indicating a tradeoff between precision and recall. Despite a commendable overall accuracy of 94%, the class imbalance within the dataset (92,200 'Running' instances vs. 126 'Failure' instances) remains a contributing factor to the model's limitations. Q3)  \nMachine learning algorithm performance varies, with Catboost excelling inaccuracy and failure detection. The choice of algorithm and continuous model refinement are critical for enhanced predictive accuracy in industrial contexts. The main conclusions are: Q1) Addressing outliers in data preprocessing significantly enhances the accuracy of machine failure prediction models. ","cbCaiccjvkURg8o4","https://ap.wps.com/l/cbCaiccjvkURg8o4","pdf",2696005,1,96,"English","en",105,"# ABSTRACT\n# LIST OF FIGURES\n# CHAPTER ONE: INTRODUCTION\n## Background\n## Problem Statement\n## Research Question\n## Summary\n## Organization of the Project\n# CHAPTER TWO: LITERATURE REVIEW","[{\"question\":\"How does input data quality affect machine failure prediction models?\",\"answer\":\"Outliers and noise significantly influence accuracy and reliability. The study reports that correcting right-skewed sensor distributions centralizes variables and improves usable patterns for prediction.\"},{\"question\":\"What role does SMOTE and feature engineering play in the proposed approach?\",\"answer\":\"Because failure instances are rare, SMOTE combined with feature engineering supports better data balancing. This improves learning despite persistent difficulty in correctly classifying failure events.\"},{\"question\":\"Which machine learning algorithm performed best for predicting failures?\",\"answer\":\"CatBoost showed the strongest performance for failure detection, with reported high accuracy and strong correctness for failure identification. The study also emphasizes that continuous model refinement and exploration of other data/algorithms remain important.\"}]","EARLY-WARNING PREDICTION FOR MACHINE FAILURES IN AUTOMATED INDUSTRIES USING ADVANCED MACHINE LEARNING TECHNIQUES | PDF",1785726728,242,{"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},"early-warning-prediction-for-machine-failures-in-automated-industries-using-advanced-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/early-warning-prediction-for-machine-failures-in-automated-industries-using-advanced-machine-learning-techniques/119868/",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","2026-08-03",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},"How does input data quality affect machine failure prediction models?","Question",{"text":75,"@type":76},"Outliers and noise significantly influence accuracy and reliability. 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