Customer Background

A leading smart manufacturing enterprise in East China operates 5 automated production lines across processes including injection molding, assembly, and inspection. Each line deploys dozens of diverse sensors and devices using industrial protocols such as Modbus, CAN, and Profinet. The company aims to achieve real-time data collection, local AI analysis, and anomaly detection to minimize downtime and boost production efficiency.

Challenges

• Multi-protocol compatibility: Supports Modbus RTU/TCP, CAN bus, Profinet, OPC UA, and other industrial protocols simultaneously.

• Real-time requirement: Data collection and analysis must be completed in milliseconds to detect anomalies and trigger alerts promptly.

• Harsh Environment Adaptation: Factory environments experience wide temperature fluctuations and strong electromagnetic interference. Equipment must meet industrial-grade reliability standards.

• Edge AI Inference: Requires running machine learning models locally for anomaly detection, demanding significant compute power.

Core challenge: Multi-protocol compatibility, real-time analysis, and edge AI versus industrial-grade reliability and cost control.

Solutions

Develop a customized industrial edge AI gateway featuring a high-performance ARM processor with NPU architecture, built-in multi-protocol conversion engine and edge AI inference framework, supporting Docker containerized deployment.

• Multi-protocol support: Built-in Modbus, CAN, Profinet, and OPC UA stacks for automatic device detection and compatibility.

• Edge AI Engine: Integrates TensorFlow Lite and ONNX Runtime for deploying common machine learning models.

• Industrial-grade design: Wide operating temperature range (-20°C to 70°C) and EMC compliance with industrial standards.

• Containerized deployment: Supports Docker for easy algorithm updates and feature scaling.

• Local Data Storage: Built-in eMMC storage supports offline resume and data caching.

Deployment Process

Hardware Deployment

Deploy 2-3 edge gateways per production line to collect data from different process steps. Connect various sensors and PLC devices via Ethernet or serial ports.

Software Configuration

Configure data collection rules and analysis models; set anomaly thresholds and alert conditions.

Data Upload

The gateway uploads processed data to the cloud platform via MQTT. The cloud platform performs global data analytics and visualization.

Performance Comparison

Commercial Revenue

• Reduce unplanned downtime by 90%+, saving millions in maintenance costs annually.

• Productivity increased by 15%, with full capacity utilization achieved.

• Improved product quality consistency and reduced defect rate by 30%.

• Achieved predictive maintenance, shifting from reactive repairs to proactive prevention.

Technical Specifications

Data collection in industrial environments faces significant challenges: electromagnetic interference can cause communication errors, temperature fluctuations may affect device stability, and protocol implementations often vary across vendors. We ensure stable operation under harsh conditions through EMC design at the hardware level, fault-tolerant mechanisms in software, and rigorous protocol compatibility testing. For edge AI inference, we selected lightweight models that maintain high accuracy while minimizing compute requirements, enabling the gateway to operate continuously on low power.

Summary

This case demonstrates the real-world value of Industrial IoT and Edge AI in smart manufacturing. By leveraging a multi-protocol industrial edge AI gateway, we enable enterprises to capture production line data in real time and perform intelligent analytics, significantly reducing unplanned downtime while boosting productivity and product quality. For manufacturers, this digital transformation is not just a technology upgrade—it's a strategic redefinition of competitiveness. As Industry 4.0 advances, similar edge intelligence solutions will be adopted across more industries.