scholarly journals Vibration based real time brake health monitoring system – A machine learning approach

2019 ◽  
Vol 624 ◽  
pp. 012027 ◽  
Author(s):  
T M Alamelu Manghai ◽  
R Jegadeeshwaran
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring System ◽  
Health Monitoring ◽  
Learning Approach ◽  
Health Monitoring System ◽  
System A ◽  
Machine Learning Approach

scholarly journals Real Time Health Monitoring System: A Review

2017 ◽  
Vol Volume-2 (Issue-1) ◽  
pp. 820-823
Author(s):  
Ajinkya Anant Bandegiri ◽  
Pradip Chandrakant Bhaskar ◽  
Keyword(s):  
Real Time ◽  
Monitoring System ◽  
Health Monitoring ◽  
Health Monitoring System ◽  
System A

Real Time Water Treatment Plant Monitoring System using IOT and Machine Learning Approach

2021 ◽  
Author(s):  
S J Sugumar ◽  
Sahana R ◽  
Saketh Phadke ◽  
Sameer Prasad ◽  
Srilakshmi G R
Keyword(s):  
Machine Learning ◽  
Water Treatment ◽  
Real Time ◽  
Monitoring System ◽  
Treatment Plant ◽  
Water Treatment Plant ◽  
Learning Approach ◽  
Machine Learning Approach ◽  
Plant Monitoring

Real Time Health Monitoring Using IoT With Integration of Machine Learning Approach

2021 ◽  
pp. 249-259
Author(s):  
K.G. Maheswari ◽  
G. Nalinipriya ◽  
C. Siva ◽  
A. Thilakesh Raj
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Health Monitoring ◽  
Learning Approach ◽  
Machine Learning Approach

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.

scholarly journals SME 4.0: Machine learning framework for real-time machine health monitoring system

2021 ◽  
Vol 1911 (1) ◽  
pp. 012026
Author(s):  
K Velmurugan ◽  
P Venkumar ◽  
Pandian R Sudhakara
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring System ◽  
Health Monitoring ◽  
Time Machine ◽  
Health Monitoring System ◽  
Learning Framework ◽  
Machine Health Monitoring ◽  
Machine Health

Real-Time Cardiovascular Health Monitoring System Using IoT and Machine Learning Algorithms

2020 ◽  
pp. 145-162
Author(s):  
T. Vairam ◽  
S. Sarathambekai ◽  
K. Umamaheswari ◽  
R. Jothibanu ◽  
D. Manojkumar
Keyword(s):  
Machine Learning ◽  
Real Time ◽  
Monitoring System ◽  
Health Monitoring ◽  
Cardiovascular Health ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Health Monitoring System

scholarly journals Heart Disease Detection by Using Machine Learning Algorithms and a Real-Time Cardiovascular Health Monitoring System

2018 ◽  
Vol 06 (04) ◽  
pp. 854-873 ◽  
Author(s):  
Shadman Nashif ◽  
Md. Rakib Raihan ◽  
Md. Rasedul Islam ◽  
Mohammad Hasan Imam
Keyword(s):  
Machine Learning ◽  
Heart Disease ◽  
Real Time ◽  
Monitoring System ◽  
Health Monitoring ◽  
Cardiovascular Health ◽  
Learning Algorithms ◽  
Machine Learning Algorithms ◽  
Disease Detection ◽  
Health Monitoring System

The impact of economic plans on the Chinese education system: a machine learning approach

CADMO ◽  
2018 ◽  
pp. 37-49
Author(s):  
Wenjun Lin ◽  
Xuefu Xu ◽  
Francesco Dell’Anna
Keyword(s):  
Machine Learning ◽  
Education System ◽  
Learning Approach ◽  
Chinese Education ◽  
System A ◽  
Machine Learning Approach ◽  
The Impact
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