[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"doc-detail-122701-en":3,"doc-seo-122701-105":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":4,"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},122701,962075006959,"Anda","https://ap-avatar.wpscdn.com/avatar/e0002397efbe92a78e?_k=1776741047341049297",8,"Research & Report","Integrating Sensor Data and Machine Learning for Predictive Maintenance in Industry 4.0 - Research paper","Manufacturing uptime depends on reliable machinery performance and minimizing unscheduled downtime, making predictive maintenance a key capability as industrial systems become increasingly complex. Advances in sensors and Industry 4.0 technologies improve access to data from equipment, processes, and products, enabling electric motor condition monitoring to reduce losses from unexpected failures and increase system dependability. The study proposes an Enhanced Naïve Bayes Artificial Neural Network (ENBANN) machine learning architecture, tested via sensor-based data collection, analysis, and comparison with simulation outputs, with Azure Cloud support for heterogeneous sensor and PLC data; preliminary results indicate accurate prediction of machine states.","Vol. 05, No. S1 (2023) 55-62, doi: 10.24874/PES.SI.01.007  \nProceedings on Engineering Sciences  \n[www.pesjournal.net](www.pesjournal.net)  \nINTEGRATING SENSOR DATA AND MACHINE LEARNING FOR PREDICTIVE MAINTENANCE IN  \nINDUSTRY 4.0  \nManish Shrivastava 1  \nPriyank Singhal Received 25.04.2023.  \nBhuvana J. Accepted 23.06.2023.  \nKeywords:  \nPredictive maintenance, machine learning, sensor data, enhanced naïve bayes artificial neural network (ENBANN), industry 4.0 technologie.  \nA B S T R A C T  \nThe availability of manufacturing machinery is crucial for having a productive production line. So, for industrialists, being successful in the field of maintenance is crucial if they want to make sure that key equipment is performing as it should and that unscheduled downtime is kept to a minimum. Predictive maintenance skills are viewed as being essential with the rise of complex industrial processes. The assistance that contemporary value chains may provide for a company's maintenance role is another area of focus. The development of sensors and Industry 4.0 technologies has greatly improved access to data from equipment, processes, and products. Electric motor condition monitoring and predictive maintenance help the industry avoid significant financial losses brought on by unforeseen motor breakdowns and significantly increase system dependability. This research offers Enhanced Nave Bayes Artificial Neural Network-based machine learning architecture for Predictive Maintenance. The system was tested in an industrial setting by building a data collection and analysis system using sensors, analyzing the data with a machine learning approach, and comparing the results to those generated by a simulation tool. With the help of the Azure Cloud, the Data Analysis Tool may access information collected by a wide variety of sensors, machine PLCs, and communication protocols. Preliminary results show that the method correctly predicts a wide range of machine states.  \n© 2023 Published by Faculty of Engineering  \n1. INTRODUCTION  \nPredictive maintenance (PM), often known as \"online monitoring,\" \"risk-based maintenance,\" or \"conditionbased maintenance,\" has been the focus of several recent research publications and has a long history (Erbiyik 2022). It speaks ofthe careful monitoring of machinery to prevent future breakdowns. The original method of predictive maintenance, visual inspection, has evolved into automated systems employing cutting-edge signal  \nprocessing methodologies based on recognition of patterns and neural networks, fuzzy logic, machine learning, etc.(Benardos and Vosniakos 2003). Many companies can detect and gather sensitive information from equipment, primarily motors, using automated ways, although human eyes and ears may no longer be able to do so. Predictive maintenance, when used in conjunction with integrated sensors, may decrease machine downtime, prevent needless equipment replacement, identify the source of a problem, and ultimately save money and increase  \nproductivity (Daily and Peterson 2017) By way of planning the protection work to prevent machine breakdowns, predictive maintenance, and preventive maintenance has certain similarities. Predictive maintenance programs, as opposed rely on data acquired by sensors and analytical algorithms, as opposed to conventional preventive maintenance. (Cheng et al., 2020) Induction motors account for over seventy percent of all electricity-powered loads in process industries. In this context, there has been a lot of interest in finding enhanced techniques to assess the health of these motors. The most prevalent reason for motor failure and the most frequent upkeep issue are both recognized as bearing failure. Predictive maintenance hence primarily emphasizes two points: increased energy efficiency and less unplanned downtime (Nacchia et al., 2021)  \nThe data-driven strategy sometimes addressed as the data mining strategy or the ML strategy employs historical data to develop a m","cbCaiigcW62u8qbD","https://ap.wps.com/l/cbCaiigcW62u8qbD","pdf",1196180,1,"English","en",105,"# Introduction\n# Related Works","[{\"question\":\"What problem does predictive maintenance aim to solve?\",\"answer\":\"Predictive maintenance focuses on carefully monitoring machinery to prevent future breakdowns and reduce unplanned downtime and unnecessary replacements.\"},{\"question\":\"What role do sensors and Industry 4.0 technologies play in the proposed approach?\",\"answer\":\"Sensors and Industry 4.0 technologies improve access to equipment, process, and product data, which is then analyzed using machine learning for predictive maintenance.\"},{\"question\":\"How was the proposed ENBANN-based system validated?\",\"answer\":\"The system was tested in an industrial setting by building a data collection and analysis pipeline using sensors, analyzing the data with machine learning, and comparing results with those from a simulation tool.\"}]","Integrating Sensor Data and Machine Learning for Predictive Maintenance in Industry 4.0 - 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