{"title":"A Detailed Study on Algorithms for Predictive Maintenance in Smart Manufacturing: Chip Form Classification Using Edge Machine Learning","authors":"Alessia Lazzaro;Doriana Marilena D'Addona;Massimo Merenda","doi":"10.1109/OJIES.2024.3484006","DOIUrl":null,"url":null,"abstract":"Industrial and technological evolution has led to the identification of different techniques and strategies that can best adapt to the needs of Manufacturing Industry 4.0. As industrial production has become more automated, the need for more efficient maintenance strategies has increased. Today, among the possible, several applications demonstrate how the Predictive Maintenance (PdM) strategy is the best performing. In fact, PdM makes it possible to predict an impending failure with high accuracy in order to intervene before failure occurs. This work focuses on the application of PdM technique in order to predict the type of chips produced by a lathe through a machine learning algorithm. Moreover, being our application a delay-sensitive one, to drastically decrease the time delay in prediction, our solution proposes the combination of PdM with the Edge Computing paradigm. To simulate this paradigm, the chosen machine learning models were deployed on STM microcontrollers obtaining both high accuracy (98%) and an inference time in the order of milliseconds.","PeriodicalId":52675,"journal":{"name":"IEEE Open Journal of the Industrial Electronics Society","volume":null,"pages":null},"PeriodicalIF":5.2000,"publicationDate":"2024-10-21","publicationTypes":"Journal Article","fieldsOfStudy":null,"isOpenAccess":false,"openAccessPdf":"https://ieeexplore.ieee.org/stamp/stamp.jsp?tp=&arnumber=10726785","citationCount":"0","resultStr":null,"platform":"Semanticscholar","paperid":null,"PeriodicalName":"IEEE Open Journal of the Industrial Electronics Society","FirstCategoryId":"1085","ListUrlMain":"https://ieeexplore.ieee.org/document/10726785/","RegionNum":0,"RegionCategory":null,"ArticlePicture":[],"TitleCN":null,"AbstractTextCN":null,"PMCID":null,"EPubDate":"","PubModel":"","JCR":"Q1","JCRName":"ENGINEERING, ELECTRICAL & ELECTRONIC","Score":null,"Total":0}
引用次数: 0
Abstract
Industrial and technological evolution has led to the identification of different techniques and strategies that can best adapt to the needs of Manufacturing Industry 4.0. As industrial production has become more automated, the need for more efficient maintenance strategies has increased. Today, among the possible, several applications demonstrate how the Predictive Maintenance (PdM) strategy is the best performing. In fact, PdM makes it possible to predict an impending failure with high accuracy in order to intervene before failure occurs. This work focuses on the application of PdM technique in order to predict the type of chips produced by a lathe through a machine learning algorithm. Moreover, being our application a delay-sensitive one, to drastically decrease the time delay in prediction, our solution proposes the combination of PdM with the Edge Computing paradigm. To simulate this paradigm, the chosen machine learning models were deployed on STM microcontrollers obtaining both high accuracy (98%) and an inference time in the order of milliseconds.
期刊介绍:
The IEEE Open Journal of the Industrial Electronics Society is dedicated to advancing information-intensive, knowledge-based automation, and digitalization, aiming to enhance various industrial and infrastructural ecosystems including energy, mobility, health, and home/building infrastructure. Encompassing a range of techniques leveraging data and information acquisition, analysis, manipulation, and distribution, the journal strives to achieve greater flexibility, efficiency, effectiveness, reliability, and security within digitalized and networked environments.
Our scope provides a platform for discourse and dissemination of the latest developments in numerous research and innovation areas. These include electrical components and systems, smart grids, industrial cyber-physical systems, motion control, robotics and mechatronics, sensors and actuators, factory and building communication and automation, industrial digitalization, flexible and reconfigurable manufacturing, assistant systems, industrial applications of artificial intelligence and data science, as well as the implementation of machine learning, artificial neural networks, and fuzzy logic. Additionally, we explore human factors in digitalized and networked ecosystems. Join us in exploring and shaping the future of industrial electronics and digitalization.