Predictive Maintenance for Mixed-Equipment Production Lines

A mid-sized plant runs a mix of legacy and modern machines with different sensor coverage and fragmented maintenance records. Maintenance is mostly reactive, causing frequent unscheduled stops, inflated spare-part spend, and overtime to recover throughput. This variability drives lower OEE and unpredictable delivery performance.

Start with a focused pilot on the most critical asset class to prove value, then expand across the fleet using a repeatable data and model pipeline.

• Data: combine PLC traces, vibration/temperature/power sensors, CMMS work-order histories, and operator shift notes (NLP-extracted events) into a unified asset timeline.
• Models & tooling: use hybrid time-series forecasting and anomaly-detection models plus survival/remaining-useful-life estimators; leverage transfer learning to share signal across similar assets and an explainability layer for root-cause hints.
• Deployment & workflow: run lightweight inference at the edge for real-time alerts and batch retraining centrally; integrate alerts into the CMMS to auto-create conditional work orders and spare-part reservations.
• Human-in-the-loop & governance: require maintenance engineer validation for high-cost interventions, log model decisions for audit, validate models with out-of-sample backtests, and define data retention and access controls.

Pilots typically reduce unplanned downtime on targeted assets by 20-50% and lower emergency maintenance spend by 10-30%, with OEE improvements of ~2-8 percentage points depending on line criticality. A focused pilot can pay back within 6-18 months; subsequent rollouts drive further savings through optimized spare-part inventory and less reactive labour scheduling.