Reactive maintenance fixes breakdowns. Preventive maintenance schedules them. Predictive maintenance prevents them. Indian enterprises spend 3-5% revenue on maintenance — ₹3 lakh crore annually. Predictive analytics cuts that by 20-40% by analysing sensor data, maintenance history and operating patterns to forecast failures before they occur.
EAM platforms with predictive modules turn raw data into actionable alerts. Compressor trending high vibration 3 weeks before failure. Pump drawing 15% excess current indicating impeller wear. This article covers implementation, algorithms and real ROI from Indian case studies.
Reactive → Preventive → Predictive Evolution
Reactive: Fix after failure (40% Indian plants). Preventive: Time-based PM (50%). Predictive: Condition-based (10%, growing 30% YoY). Predictive needs data: vibration, temperature, current, pressure, oil analysis, ultrasonics.
Data Sources Powering Predictions
SCADA/PLC telemetry (5-min intervals). Historical work orders (failure modes). Operating logs (load cycles). External: ambient temp, humidity. IoT sensors retrofitted on legacy assets.
Key Algorithms and Failure Indicators
Vibration: FFT spectrum analysis detects bearing faults. ML anomaly detection flags 20% current rise. Regression models predict remaining useful life (RUL). Threshold alerts + trend warnings.
EAM + Predictive Module Workflow
Sensors → EAM asset record → ML engine → health score (0-100) → work order if <70. Auto-schedule oil sampling at vibration RMS >2.5.
Real ROI: Indian Manufacturing Cases
Steel plant: Turbine failure prevented, saved ₹8 crore. Cement: 25% PM reduction. Chemical: Compressor RUL accuracy 87%.
Practical Implementation Steps
Pilot 10 critical assets. Baseline PM cost. Install sensors. Train ML on 12 months data. Scale to 100 assets Year 2.
Retrofitting Legacy Assets
Wireless vibration/current sensors (₹5k/unit). 24-month payback. No PLC disruption.