The Learning Path to Neural Network Industrial Application in Distributed Environments

Processes

Lenka Landryová¹  Jan Sikora¹  Renata Wagnerová¹ 

[1] Department of Applied Mechanics, Faculty of Mechanical Engineering, VŠB—Technical University of Ostrava, 17. Listopadu 2172/15, 708 00 Ostrava, Czech Republic;

关键词: distributed systems;    big data;    machine learning;    data visualization;    process control;    algorithm;   

DOI  :  10.3390/pr9122247

来源: DOAJ

【 摘 要 】

Industrial companies focus on efficiency and cost reduction, which is very closely related to production process safety and secured environments enabling production with reduced risks and minimized cost on machines maintenance. Legacy systems are being replaced with new systems built into distributed production environments and equipped with machine learning algorithms that help to make this change more effective and efficient. A distributed control system consists of several subsystems distributed across areas and sites requiring application interfaces built across a control network. Data acquisition and data processing are challenging processes. This contribution aims to present an approach for the data collection based on features standardized in industry and for data classification processed with an applied machine learning algorithm for distinguishing exceptions in a dataset. Files with classified exceptions can be used to train prediction models to make forecasts in a large amount of data.

【 授权许可】

Unknown