Zero Unplanned Downtime: ML-Driven Predictive Maintenance

Vibration analysis, thermal monitoring, and remaining useful life prediction — how machine learning catches failures before they happen.

The Cost of Unplanned Downtime

Unplanned downtime costs manufacturers an average of $260,000 per hour. Beyond the direct costs, there are cascading effects: missed deadlines, expedited shipping fees, customer dissatisfaction, and overtime labor costs. Predictive maintenance eliminates these surprises.

How Predictive Maintenance Works

Instead of waiting for equipment to fail (reactive) or servicing on a fixed schedule (preventive), predictive maintenance uses machine learning to analyze real-time sensor data and predict when a component will actually fail.

Vibration Analysis

Accelerometers mounted on rotating equipment capture vibration signatures. ML models trained on historical failure data can detect bearing wear, misalignment, and imbalance weeks before they cause breakdowns.

Thermal Monitoring

Infrared sensors and thermal cameras detect abnormal heat patterns in motors, transformers, and electrical panels. Overheating is often the first sign of impending failure — and thermal monitoring catches it early.

Remaining Useful Life (RUL) Prediction

OnWebApp's ML models calculate the remaining useful life of critical components based on operating conditions, usage patterns, and environmental factors. Maintenance is scheduled at the optimal moment — not too early (wasting resources) and not too late (risking failure).

Integration with ERP

Predictive maintenance insights feed directly into your ERP system. Work orders are generated automatically, spare parts are ordered in advance, and maintenance windows are scheduled during planned downtime.

Proven Results

• 70% reduction in unplanned downtime
• 25% extension of equipment lifespan
• 35% reduction in maintenance costs
• Zero safety incidents from equipment failure