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Development of Robust IoT Sensor Nodes for Predictive Maintenance in Loading Dock Equipment

verytechnology.com

Manufacturing

Logistics

Information technology

Technical Challenges in IoT Sensor Integration

Ultrasonic welding processes for IoT device assembly caused malfunction in sensitive accelerometers, leading to calibration failures and inaccurate equipment health monitoring. Existing solutions failed to balance durability with sensor precision.

About the Client

Market leader in loading dock equipment seeking to expand IoT-enabled predictive maintenance solutions

Key Project Goals

Develop modular sensor nodes resistant to manufacturing stresses
Implement predictive maintenance capabilities through real-time data analytics
Ensure compatibility between ultrasonic welding and sensitive IMU components
Optimize battery longevity through edge computing
Create scalable IoT architecture for enterprise deployment

Core System Requirements

Modular sensor node enclosures with 6-axis IMU
LoRaWAN-to-Azure IoT Hub data transmission
On-device machine learning for anomaly detection
Energy-efficient edge computing architecture
Customizable operational alert thresholds

Technology Stack

LoRaWAN

Azure IoT Hub

MQTT

Docker

Zephyr RTOS

KerOS (Linux)

System Integrations

Cloud analytics platforms
Existing facility management systems
Predictive maintenance dashboards

Non-Functional Requirements

Vibration-resistant hardware design
Scalable to 30,000+ assets
IP67 environmental rating
10-year battery life expectancy
Sub-50ms sensor response latency

Expected Business Impact

Enables transition from reactive to predictive maintenance, reducing equipment downtime by 40-60%, lowering maintenance costs by 30%, and enhancing operational safety standards. Positions client as market leader in IoT-enabled loading dock solutions with expansion potential to 30,000+ assets.