Preventive Maintenance

Use AI and IoT to predict and prevent equipment failures, reducing downtime and costs

Business impact

• Maintenance costs — Lowered by reducing unnecessary repairs and preventing major breakdowns
• Downtime reduction — Minimized through early detection and timely maintenance scheduling
• Operational efficiency — Improved by maintaining equipment reliability and reducing disruptions
• Equipment uptime — Increased by preventing unexpected failures and optimizing asset availability
• Safety incidents — Decreased by early identification of hazardous equipment conditions
• Maintenance planning efficiency — Enhanced by data-driven scheduling and resource allocation

Data requirements

• IoT sensors (Numeric) — Collect real-time data on temperature, vibration, pressure, and operational metrics
• Historical maintenance logs (Structured) — Provide context and labels for training predictive models
• Operational environment data (Numeric) — Include environmental conditions affecting equipment degradation
• 3D imaging and sonar (Image) — Enable detailed asset inspections and anomaly detection
• Audio sensors (Audio) — Capture acoustic signals indicative of equipment faults

AI methods and techniques

• Predictive AI — Used to forecast equipment failures and remaining useful life from sensor data
• Generative AI — Supports anomaly detection and simulation of asset conditions for maintenance planning
• Agentic AI — Enables autonomous inspection robots to perform routine asset health monitoring

AI models and model families

Transformer, Random Forest, XGBoost, LSTM, Neural Networks, Isolation Forest

Industries

Real-world evidence

11 documented case studies on record.

Companies using this: ANYbotics, Douglas Rulmeca, Eagle Technology Inc, Enerjisa, Ford Motor, IQUA Robotics, John Deere, Mars Wrigley, Precision Pulley & Idler, Rolls Royce, Schneider Electric.