Predictive Maintenance AI

An advanced predictive maintenance system combining IoT sensors, edge computing, and machine learning to predict equipment failures before they occur. The system monitors critical machinery in real-time, analyzes patterns, and alerts maintenance teams with precise failure predictions and recommended actions.

A large manufacturing facility experienced frequent unplanned downtime costing millions annually. Traditional preventive maintenance was inefficient—either too frequent (wasting resources) or too infrequent (leading to failures). They needed a data-driven approach to optimize maintenance schedules and prevent catastrophic failures.

We deployed IoT sensors across 200+ pieces of critical equipment, monitoring vibration, temperature, pressure, and other parameters. Edge computing devices process data locally for immediate anomaly detection, while cloud-based ML models analyze historical patterns to predict failures 2-4 weeks in advance. A custom dashboard provides maintenance teams with prioritized alerts and recommended actions.

• Real-time sensor data collection and processing
• Edge-based anomaly detection
• ML-powered failure prediction (2-4 weeks advance notice)
• Prioritized maintenance alerts
• Historical trend analysis
• Mobile app for field technicians
• Integration with CMMS systems
• Automated work order generation
• ROI tracking and reporting