Predictive Maintenance with Machine Learning on the Edge
Driving efficiency in factories with predictive maintenance and intelligent ML alerts
The Challenge
Our Approach
We developed a predictive maintenance system that used supervised learning and time-series forecasting models trained on vibration, temperature, pressure, and runtime data. Key features included:
- LSTM-based time-series models deployed on edge devices (NVIDIA Jetson)
- Anomaly detection using autoencoders and isolation forests
- Dynamic retraining pipeline with cloud sync for fleet-wide learning
- Custom dashboard with maintenance lead time alerts for technicians
Stats
The Outcome
- Reduced unplanned downtime by 65% in 6 months
- Predicted 80% of critical component failures at least 48 hours in advance
- Increased machine lifespan by 15–20% through timely intervention
- Empowered maintenance teams with real-time actionable alerts
Case Study
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