scholarly journals ATHMoS: Automated Telemetry Health Monitoring System at GSOC using Outlier Detection and Supervised Machine Learning

2016 ◽  
Author(s):  
Corey OMeara ◽  
Leonard Schlag ◽  
Luisa Faltenbacher ◽  
Martin Wickler
Keyword(s):  
Machine Learning ◽  
Outlier Detection ◽  
Monitoring System ◽  
Health Monitoring ◽  
Supervised Machine Learning ◽  
Health Monitoring System ◽  
Automated Telemetry

Machine Learning based Health Monitoring System

2020 ◽  
Vol 24 ◽  
pp. 1788-1794
Author(s):  
K. Gnana Sheela ◽  
Anu Rose Varghese
Keyword(s):  
Machine Learning ◽  
Monitoring System ◽  
Health Monitoring ◽  
Health Monitoring System

scholarly journals Real-time Machine Health Monitoring System using Machine Learning with IoT Technology

2021 ◽  
Vol 335 ◽  
pp. 02005
Author(s):  
Tzen Ket Wong ◽  
Hou Kit Mun ◽  
Swee King Phang ◽  
Kai Lok Lum ◽  
Wei Qiang Tan
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring System ◽  
Health Monitoring ◽  
Industry 4.0 ◽  
Phase System ◽  
Time Machine ◽  
Health Monitoring System ◽  
Machine Health Monitoring ◽  
Machine Health

Machine health monitoring is the main focal point for now as many industries are evolving to industry 4.0. Industry 4.0 is the revolution in industrial that involve the Internet of Things (IoT) and artificial intelligence toward automation and data sharing for production efficiency improvement. The existing established methods for machine health monitoring were not in real-time and there was no real-time correction of data from the load and processing of data on the computer. In tracking machine health efficiency this approach wasn’t very successful. Real-time machine health monitoring can improve overall equipment effectiveness (OEE), reduce electricity consumption, minimize unplanned downtime, and extend machine lifetime. In this research paper, we propose to design a real-time machine health monitoring system using machine learning with IoT technology that can analyze the supply balancing condition on a 3-phase system. This system is built with compact physical hardware and can capture the electrical data from the load then send it to the server. The server will progress data and train the data using machine learning. The system was installed on a blender machine in a factory. In this research, a system which is able to monitor the machine operation and classify the operation stages of the machine was developed. Besides that, the system also capable to monitor the load balancing condition of the machine.

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