一种基于机器学习的状态监测云架构

Fernando Arévalo, Mochammad Rizky Diprasetya, Andreas Schwung
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引用次数: 6

摘要

在行业数字化的框架下,使用机器学习技术来评估状态监测、故障检测和流程优化的趋势越来越大。传统方法使用集中在服务器中的本地信息技术(IT)框架来提供这些服务。设备和IT人力的成本与基于机器学习的状态监测的实施有关。如今,云计算可以用远程服务取代本地IT框架,远程服务可以根据客户需求付费。本文提出了一种基于机器学习的状态监测云架构,最终用户可以通过web应用程序对其进行评估。采用融合分类方法实现状态监测。采用邓普斯特-谢弗证据理论(DSET)实现融合。结果表明,DSET的使用提高了整体结果。
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A Cloud-based Architecture for Condition Monitoring based on Machine Learning
In the framework of the digitalization of the industry, there is an increasing trend to use machine learning techniques to assess condition monitoring, fault detection, and process optimization. Traditional approaches use a local Information Technology (IT) framework centralized in a server in order to provide these services. Cost of equipment and IT manpower are associated with the implementation of a condition monitoring based on machine learning. Nowadays, cloud computing can replace local IT frameworks with a remote service, which can be paid according to the customer needs. This paper proposes a cloud-based architecture for condition monitoring based on machine learning, which the end-user can assess through a web application. The condition monitoring is implemented using a fusion of classification methods. The fusion is implemented using Dempster-Shafer Evidence Theory (DSET). The results show that the use of DSET improves the overall result.
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