Data analysis and machine learning-based modeling for real-time production





Data analysis, Machine learning, Fault detection

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Research article


  • S. C. Prabha Department of Electrical Communication Engineering, KPR Institute of Engineering and Technology, Coimbatore, Tamil Nadu, India.
  • P. Sivaraaj Department of Forensic Science, KARE, Krishnankoil, Tamil Nadu, India.
  • S. Kantha Lakshmi Department of Electrical & Electronics Engineering, PSG College of Technology, Coimbatore, Tamil Nadu, India


This article focuses on data analysis and real-time data modeling using linear regression and decision tree algorithms that might make revolutionary predictions on production data. Factual time data points, including temperature, load, and warning on all the presented axis, are the dependent parameters which be contingent on the changes in the autonomous paraments like load. Monitoring and innovative prediction are very much needed in industry as there are recurrent load changes that would create a data drift and, in terms of maintenance, that could impact the production side, the need for continuous monitoring and control. Machine learning-based approaches would work better on these real-time production datasets

How to Cite

Prabha, S. C., Sivaraaj, P., & Lakshmi, S. K. (2023). Data analysis and machine learning-based modeling for real-time production. The Scientific Temper, 14(03), 637–640.


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