Multi-agent system for anomaly detection in Industry 4.0 using Machine Learning techniques

Abstract

Industry 4.0 is the new industrial stage that is committed to greater automation, connectivity and globalization. The interrelation between the different areas has penetrated the industrial world thanks to the Internet of things and the world of Big Data. This amount of information available in plants is growing increasingly, also aided by the network computing services offered by cloud computing or edge computing. That is why it’s necessary to carry out complex fusion methods and data analysis using Machine Learning techniques to address specific industrial requirements and needs. The central challenge of industry 4.0 from the perspective of data science is to predict the history within the monitored processes, providing as much information as possible, avoiding them and stave off severe economic losses. This article will show a review of the application of Artificial Intelligence (AI) techniques such as Machine Learning (ML) immersed in multi-agent systems (MAS) in Industry 4.0. For this, a bibliographic search has been carried out in databases recognized as Science Direct, Google Scholar, Scopus or Springer, filtering the investigations from 2018 to the actually. The article concludes by pointing out the possible future lines and the importance of the transition towards the implementation of new technologies for the competitiveness of factories.

Keywords

• industry 4.0
• multiagent system
• machine learning
• failure diagnosis