Fixitbeforeitbreaks.Predictbeforeitfails.
Predictive maintenance and anomaly detection systems that use sensor data and ML to predict equipment failures 2–4 weeks before they occur — eliminating unplanned downtime and reducing maintenance costs.
What this means for your business
Unplanned equipment downtime costs industrial companies an average of $260,000 per hour. Traditional preventive maintenance — servicing equipment on fixed schedules regardless of actual condition — wastes resources on unnecessary maintenance while still missing failures that occur between service intervals. Predictive maintenance uses sensor data and machine learning to predict failures based on actual equipment condition — servicing only when needed, and preventing failures before they occur.
Digital Prizm has deployed predictive maintenance systems for manufacturing plants, logistics fleets, utility infrastructure, and building systems — consistently achieving 40–60% reduction in unplanned downtime and 20–30% reduction in total maintenance costs.
Why act now?
Predictive maintenance is the highest-ROI AI application in industrial settings. The technology is mature, the sensor hardware is affordable, and the ROI is proven: 45% reduction in unplanned downtime, 25% reduction in maintenance costs, 2–4 weeks advance warning. Every unplanned failure that occurs while a predictive system is not in place is a preventable cost.
Market Readiness
Adoption vs ROI by Domain
Our Capabilities
Sensor Data Ingestion
Real-time ingestion of vibration, temperature, pressure, current, and acoustic sensor data from industrial equipment — at millisecond resolution.
Anomaly Detection Models
ML models that learn normal equipment behavior and detect deviations that indicate developing faults — with configurable sensitivity and alert thresholds.
Remaining Useful Life Prediction
Prognostic models that estimate how long equipment will continue operating before failure — enabling planned maintenance scheduling.
Fault Classification
Models that identify the specific type of fault developing — bearing wear, imbalance, misalignment, electrical fault — enabling targeted maintenance actions.
Maintenance Work Order Integration
Automatic generation of maintenance work orders in your CMMS when anomalies are detected — closing the loop between prediction and action.
Digital Twin Integration
Real-time digital twins of monitored equipment that visualize sensor data, anomaly scores, and predicted failure timelines in an intuitive interface.
Technology Stack
The platforms, frameworks, and tools we use to deliver this capability
Industries we serve with this technology
Frequently Asked Questions
For rotating equipment, vibration and temperature sensors are the most valuable. We assess your equipment and recommend the minimum sensor set for maximum predictive value — often leveraging sensors already installed.
Ready to explore this technology?
Schedule a consultation with our emerging technology specialists. We'll assess your readiness and propose a practical adoption roadmap.
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