A Detailed Study on Algorithms for Predictive Maintenance in Smart Manufacturing: Chip Form Classification Using Edge Machine Learning

IF 5.2 Q1 ENGINEERING, ELECTRICAL & ELECTRONIC
Alessia Lazzaro;Doriana Marilena D'Addona;Massimo Merenda
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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.
智能制造中预测性维护算法的详细研究:利用边缘机器学习进行芯片形状分类
工业和技术的发展促使人们找出了最能适应制造业 4.0 需求的不同技术和策略。随着工业生产的自动化程度不断提高,对更高效的维护策略的需求也随之增加。如今,在各种可能的策略中,有几种应用证明了预测性维护(PdM)策略的最佳性能。事实上,PdM 可以高精度地预测即将发生的故障,以便在故障发生前进行干预。这项工作的重点是应用 PdM 技术,通过机器学习算法预测车床产生的切屑类型。此外,由于我们的应用是对延迟敏感的应用,为了大幅减少预测的时间延迟,我们的解决方案建议将 PdM 与边缘计算范例相结合。为了模拟这种模式,我们在 STM 微控制器上部署了所选的机器学习模型,获得了较高的准确率(98%)和毫秒级的推理时间。
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来源期刊
IEEE Open Journal of the Industrial Electronics Society
IEEE Open Journal of the Industrial Electronics Society ENGINEERING, ELECTRICAL & ELECTRONIC-
CiteScore
10.80
自引率
2.40%
发文量
33
审稿时长
12 weeks
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.
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