Industrial IoT
FactorySense Predictive Maintenance
A predictive maintenance system for manufacturing equipment using vibration and thermal analysis.
Precision Manufacturing Co.
February 2024
6 months
Project Overview
This Industry 4.0 solution monitors 200+ machine parameters in real-time using edge computing devices. Machine learning models predict equipment failures 7-14 days in advance, with automated work order generation. The system includes digital twin visualization, energy consumption analytics, and OEE (Overall Equipment Effectiveness) tracking across multiple factory locations.
Challenges
- Processing high-frequency vibration spectra on edge devices
- Synchronizing digital twins across multiple factory locations
- Maintaining prediction accuracy during equipment wear-and-tear
- Securing OT networks against ransomware attacks
Solutions
- Developed FFT optimization algorithms for ARM-based edge processors
- Implemented operational transformation (OT) protocols for twin synchronization
- Created continuous retraining pipelines using equipment degradation models
- Deployed unidirectional gateway diodes with protocol-aware deep packet inspection
Technologies Used
"This system transformed our maintenance from reactive to predictive, saving millions in lost production while improving equipment lifespan."
Robert Chen
Plant Manager, Precision Manufacturing
Project Details
Client
Precision Manufacturing Co.
Duration
6 months
Team Size
8 IoT specialists
Budget
$420,000
Completion
February 2024
Key Features
Predictive Failure Alerts
Digital Twin Visualization
Energy Analytics
OEE Tracking
Automated Work Orders
Edge Computing
Multi-site Dashboard
Achievements
45% reduction in unplanned downtime
18% increase in OEE
$2.3M annual savings
ROI in 5 months
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