A Cloud-based Architecture for Condition Monitoring based on Machine Learning

被引:0
作者
Arevalo, Fernando [1 ]
Diprasetya, Mochammad Rizky [1 ]
Schwung, Andreas [1 ]
机构
[1] South Westphalia Univ Appl Sci, Dept Automat Technol, Iserlohn, Germany
来源
2018 IEEE 16TH INTERNATIONAL CONFERENCE ON INDUSTRIAL INFORMATICS (INDIN) | 2018年
关键词
Cloud-based Architecture; Condition Monitoring; Machine Learning; IaaS; MQTT; OPC-UA; DSET;
D O I
暂无
中图分类号
TP301 [理论、方法];
学科分类号
081202 ;
摘要
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.
引用
收藏
页码:163 / 168
页数:6
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