Project Goals


  • Equipment status identification
  • Feed rate prediction from energy
  • G-code interpretation


  • G-code interpretation and energy prediction
  • Equipment anomaly detection
  • Realization of developed methodologies - IIoT


  • Real-time energy monitoring
  • Real-time anomaly detection
  • Equipment's operations optimization

Research team: Vignesh Selvaraj


IIoT Dashboards

Real-time energy data from FANUC ROBONANO located at MINLAB - Energy Monitoring Dashboard

Real-time anomaly detection of FANUC ROBONANO located at MINLAB - Equipment Status

Why do we do it?

Manufacturing is responsible for almost 45% of total energy consumption in the US and a similar portion in other countries. Machine energy consumption accounts for a big portion of 45% and thus understanding how machine tools consume energy helps to minimize energy consumption by design optimization of the machine tools, strategic operation control, energy balance on the production line, and energy footprint and control of supply chain. Monitoring and control of horizontal and vertical integration of total manufacturing infra can be achieved by IIoT (Industrial Internet of Thing) with a similar manufacturing paradigm called smart manufacturing, digital manufacturing, and industry 4.0.

More details

Some Case Studies

Identifying the equipment state just from the energy consumption data.

Energy Framework
Framework for energy monitoring

A real-time anomaly detection system for manufacturing machines.

Framework - Machine Fault Detection
Framework for a machine fault detection system with integrated model monitoring