Industry 4.0 based Machine Learning Models for Anomalous Product Detection and Classification

Sourabh Kumar, S. K. Chandra, R. Shukla, Lipismita Panigrahi
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Abstract

Automation has made tremendous changes in the industries. It has been used to automate the manual processes involved in different physical units of the industries. The purpose was to increase the production in the manufacturing. Now, Computers are being used in the industries to monitor functionalities of different production units with the help of artificial intelligence and internet of things (IoT). The IoT has revolutionized the industries. It is an interconnected network system of physical units. The core purpose of it to gather and share information among different physical units. The IoT has great impact on the many areas such as business, industry, medicine, the economy, transport, industrial robots and automation systems. IoT with artificial intelligence has wide range of industrial applications. Industry 4.0 is used in the industries where different industrial units are connected over the internet and interacting to make decisions via machine-to-machine communication. It has increased the benefits of industries in terms of production and supply chain management. Manufacturing industry monitors its production units in every 10 milliseconds to capture features of the product that is being produced. The features generated in this process are huge in amount. Critical observation is performed on the generated features to categorize the product as anomalous or good. Product classification is difficult task in the labeled datasets due to human bias in labeling the final product as anomalous or good. In this work, machine learning models is being used to detect and classify faulty product produced by manufacturing industry. Both qualitative and quantitative study will be carried out to compare various machine learning models.
基于工业4.0的异常产品检测和分类机器学习模型
自动化给工业带来了巨大的变化。它已被用于自动化工业中不同物理单元中涉及的手动过程。目的是为了提高制造业的产量。现在,在人工智能和物联网(IoT)的帮助下,计算机正在工业中用于监控不同生产单元的功能。物联网已经彻底改变了行业。它是一个相互连接的物理单元网络系统。它的核心目的是在不同的物理单元之间收集和共享信息。物联网对商业、工业、医药、经济、交通、工业机器人和自动化系统等诸多领域产生了巨大影响。具有人工智能的物联网具有广泛的工业应用。工业4.0用于不同工业单元通过互联网连接并通过机器对机器通信进行交互以做出决策的行业。它增加了工业在生产和供应链管理方面的利益。制造业每隔10毫秒对其生产单元进行监控,以捕捉正在生产的产品的特征。在这个过程中产生的特征数量巨大。对生成的特征进行关键观察,以将产品分类为异常或良好。由于人类在将最终产品标记为异常或良好时存在偏见,因此在标记数据集中进行产品分类是一项困难的任务。在这项工作中,机器学习模型被用于检测和分类制造业生产的故障产品。将进行定性和定量研究,以比较各种机器学习模型。
本文章由计算机程序翻译,如有差异,请以英文原文为准。
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