VAMS Mechatronica
Railways

Predictive Maintenance for CNC Machines

Implemented at Modern Coach Factory (MCF), Raebareli to improve machine reliability and OEE and reduce unplanned downtime.

38%Reduction in unplanned downtime
22%Improvement in OEE
5.5 monthsReturn on investment
Overview

Client Profile

Modern Coach Factory (MCF), Raebareli, a production unit of Indian Railways, manufactures railway coaches with strict quality and precision standards. The facility operates multiple CNC machining centers that are critical to coach fabrication.

The challenge

Operational challenges

  • Frequent unplanned CNC machine breakdowns
  • Limited visibility into machine health conditions
  • A reactive maintenance approach
  • Inconsistent OEE across machines and shifts
  • Production delays caused by sudden failures
Solution

What was implemented

A real-time predictive maintenance platform was deployed to monitor CNC machine health, detect anomalies early and give maintenance teams actionable insight.

Real-time monitoring

Continuous monitoring of spindle vibration, temperature, load, power consumption and cycle times.

AI-based analytics

Machine learning models identify abnormal patterns and predict potential failures before a breakdown occurs.

Smart alerts

Threshold and trend-based alerts reach maintenance teams for early intervention.

The platform in use

ProMonitor screens

Screens are from the ProMonitor demonstration environment, which uses simulated machine data. They show the platform used for this project, not the customer’s own data.

Live machine telemetryScreenshot coming soonassets/promonitor/monitoring-machine.webp
ProMonitor live telemetry for a CNC machining center
Live machine telemetrySpindle speed, load, temperature, vibration and power for one CNC machine, with a live multi-tag chart.
Predictive maintenance overviewScreenshot coming soonassets/promonitor/pdm-overview.webp
ProMonitor predictive maintenance overview
Predictive maintenance overviewFleet health distribution, highest-risk assets, predicted failures and maintenance due.
Asset healthScreenshot coming soonassets/promonitor/pdm-asset-health.webp
ProMonitor asset health table
Asset healthHealth score, risk, failure probability and remaining useful life for every machine.
Risk matrixScreenshot coming soonassets/promonitor/pdm-risk-matrix.webp
ProMonitor asset risk matrix
Risk matrixAssets placed by probability of failure and asset criticality, so the riskiest machines are visible first.
Visibility

Dashboards and views

  • Live machine status (running, idle, alarm)
  • Machine health score and failure probability
  • Remaining useful life and risk by asset criticality
  • Alarms and maintenance work in one place
Results

Benefits

  • A shift from reactive to proactive, data-driven maintenance
  • Earlier warning of machines at risk of failure
  • One shared view of machine condition for operators and maintenance teams

Conclusion

The predictive maintenance initiative at MCF Raebareli moved maintenance from a reactive routine to a proactive, data-driven strategy, improving machine availability and overall production efficiency.

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