Predictive maintenance and energy optimization
IoT sensors on graphite furnaces, ML failure models and RL-based optimization of energy input. Proactive maintenance alerts and lower OPEX.
−51 %
unplanned downtime
−17 %
energy consumed per ton
Proactive
maintenance alerts before failure
Context
The problem
Recurring unplanned downtime
Graphite furnaces under aggressive thermal cycling: failures with no warning that halt production.
Volatile energy consumption
Energy input driven conservatively or reactively: overconsumption on certain cycles.
Underused sensor data
The historian is fed but has no predictive model connected to the CMMS to trigger work orders.
Inefficient calendar-based maintenance
Interventions too early (wasted OPEX) or too late (costly failure): no leading indicators exploited.
Solution
Deployed architecture
IoT on graphite furnaces
Temperature, current, vibration, atmosphere: continuous acquisition into the historian.
Failure prediction + RL energy
LSTM and time-series models to anticipate failures. Reinforcement learning to optimize energy input per cycle.
Proactive maintenance
Adaptive thresholds and operator/maintenance alerts before symptoms are visible in production.
CMMS and supervision
Alerts routed to the maintenance tool. Consumption dashboard per cycle and per asset.
Results
Measured results
Measured in production conditions on the deployed scope.
−51 %
Unplanned downtime
Measured over the post-deployment period vs a 12-month baseline.
−17 %
Energy / ton
Optimization of energy input per cycle without degrading product quality.
Weeks
Failure lead time
Alert window before a visible failure: work-order planning in off-peak periods.
OPEX
Targeted maintenance
Gradual replacement of pure calendar-based upkeep with condition-based maintenance.
Lessons learned
What we took away
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