#arm edge gateway

In the wave of industrial digital transformation, real-time monitoring and intelligent management of production equipment have become key drivers for enterprises to enhance efficiency and reduce costs. While traditional cloud-based monitoring offers robust functionality, it faces limitations in real-time performance, reliability, and bandwidth consumption. With the rise of edge computing, the integration of ARM edge controllers and ThingsBoard Edge provides an efficient, flexible, and highly scalable solution for production equipment status monitoring. This approach shifts computational resources to the edge, enabling local intelligent processing alongside cloud collaboration, and empowers businesses to build a resilient industrial IoT ecosystem.

Architecture Design

The solution employs a dual-layer architecture of "edge controller + edge IoT platform" to ensure seamless data acquisition, processing, and management:

ARM Edge Controller BL370 Series

Deployed on-site in production environments, it directly collects equipment runtime data (e.g., temperature, current, vibration, RPM).

Supports multiple industrial protocols (Modbus, OPC UA, MQTT, CAN, etc.), enabling seamless integration with diverse devices.

Features built-in local computing capabilities for data preprocessing, anomaly detection, and filtering, significantly reducing the upload of redundant data and improving transmission efficiency.

ThingsBoard Edge

Deployed on the edge controller or local servers as an on-site IoT platform.

Provides device management, telemetry data collection, rule engines, and local visualization features, including real-time dashboards and report generation.

Synchronizes data with the cloud-based ThingsBoard Server, supporting centralized cross-factory management and advanced big data analytics.

Application Scenarios

Real-Time Status Monitoring: Equipment operational status can be viewed locally in the workshop, ensuring full transparency in the production process.

Anomaly Alerts: The edge rule engine instantly detects deviations (e.g., overheating or abnormal vibration) and triggers local or cloud-based notifications.

Data Buffering and Resumable Uploads: During network disruptions, the edge node automatically caches data, resuming seamless synchronization to the cloud upon recovery to maintain data integrity and continuity.

Layered Management: On-site ThingsBoard Edge handles device connectivity and initial processing, while the cloud platform focuses on cross-factory data aggregation, analysis, and reporting.

Advantages Analysis

Low-Latency Response: Local processing and alerting mechanisms meet the high real-time demands of industrial scenarios, achieving millisecond-level responses.

High Reliability: Even if cloud services are unavailable, the edge layer operates independently, ensuring uninterrupted monitoring and alerts.

Flexible Scalability: ARM controllers allow dynamic interface configuration based on device types; ThingsBoard Edge supports modular rule and visualization extensions, adapting to evolving production environments.

Cost Optimization: Dramatically reduces cloud computing loads and storage needs, minimizing bandwidth usage for more economical digital deployments.

Future Outlook

As demands for on-site intelligence continue to grow in industrial settings, the fusion of ARM edge controllers and ThingsBoard Edge will expand its application boundaries:

Predictive Maintenance: Integrating edge AI models for real-time data analysis to forecast potential failures in advance, extending equipment lifespan.

Energy Optimization: Monitoring equipment energy patterns and leveraging cloud algorithms to implement energy-saving strategies, advancing green manufacturing.

Cross-Industry Expansion: Beyond manufacturing, it extends to sectors like energy, power, and transportation, fostering universal innovation in the Industrial IoT.

Conclusion

The collaborative architecture of ARM edge controllers and ThingsBoard Edge infuses production equipment status monitoring with a potent blend of real-time capability, reliability, and scalability. It not only elevates the intelligence of production sites but also lays a solid foundation for enterprise digital transformation. Looking ahead, this solution will thrive across more industries, propelling the Industrial IoT toward an era of greater intelligence and autonomy.

Case Details

Computer Numerical Control (CNC) machines are the backbone of modern precision manufacturing industries such as automotive, aerospace, electronics, and mold production. While CNC systems offer high accuracy and automation, they often operate as data islands, with limited connectivity and interoperability across different machine vendors and protocols.

To address this challenge, the ARM-based Edge Gateway provides a reliable and cost-effective solution for connecting CNC machines, integrating multiple communication standards, performing edge analytics, and securely transmitting data to higher-level systems such as MES, SCADA, and cloud platforms.

System Architecture

Data Sources (CNC & Sensors)

CNC controllers (FANUC, Siemens, Mitsubishi, Heidenhain, etc.) provide spindle speed, feed rate, axis position, alarm codes, and machining program data.

External sensors (vibration, temperature, power consumption, noise, tool wear) extend monitoring capabilities beyond the built-in CNC controller.

ARM Edge Gateway BL340 Series

Interfaces: Ethernet, RS485/RS232, Modbus, OPC UA, MTConnect.

Functions:

Protocol conversion (FOCAS, proprietary CNC data → OPC UA/MQTT)

Data preprocessing and compression

Local storage with buffering in case of network failure

Edge AI algorithms for anomaly detection, tool life prediction, and energy analysis

Secure data transmission with encryption (TLS/SSL, VPN)

Upper-Level Systems

MES (Manufacturing Execution System): production scheduling, job tracking, and OEE monitoring

SCADA: real-time visualization, alarms, and plant-wide monitoring

Cloud / Big Data / AI: predictive maintenance, energy optimization, and enterprise-wide analytics

Key Applications

Multi-Machine Connectivity Integrates CNC machines from different vendors into a unified monitoring and management platform.

Real-Time Production Monitoring Tracks spindle speed, feed rate, cutting cycle time, and tool condition for transparent shop-floor management.

Predictive Maintenance Uses vibration and current analysis to detect spindle or bearing faults before breakdown occurs.

Energy Management Collects and analyzes power consumption to identify inefficiencies and optimize cutting parameters.

Remote Diagnostics Enables OEMs and service teams to securely access machine data through VPN/4G/5G for remote troubleshooting.

Benefits

Protocol Adaptability – Seamlessly bridges heterogeneous CNC systems and industrial protocols.

Real-Time Insights – Local edge analytics reduce reliance on cloud latency.

Data Security – Encrypted communication ensures safe data transmission.

Cost Efficiency – Low power consumption and high integration make ARM platforms cost-effective.

Scalability – Supports sensor expansion and AI model deployment for future smart manufacturing needs.

Conclusion

The adoption of ARM Edge Gateways in CNC machine tools marks a significant step toward smart manufacturing and Industry 4.0. By enabling seamless data connectivity, real-time analytics, and predictive insights, ARM Edge Gateways transform CNC machines from isolated production units into intelligent, connected assets. This not only improves equipment utilization and reduces downtime but also lays the foundation for digitalized and data-driven manufacturing.

Case Details

In modern water environment management, real-time water quality monitoring is critical for ensuring ecological safety and public health. ARM IoT Gateway, with their low power consumption, high reliability, and edge computing capabilities, are ideal for deployment in rivers and lakes to build intelligent water quality monitoring systems.

Application Scenarios

River Section Monitoring: Deployed at key river sections to continuously track water quality changes.

Lake Water Monitoring: Utilizes multiple buoy nodes to form a monitoring network covering large water areas.

Water Source Protection: Real-time monitoring of drinking water sources with early warnings for pollution events.

System Architecture and Functions

Data Collection:

Employs sensors for pH, dissolved oxygen, and optical turbidity, collecting data via RS485/analog interfaces.

Supports multi-point expansion for wide-area monitoring needs.

Edge Processing:

ARM IoT Gateway BL335 series performs data filtering, anomaly detection, and local storage.

Enables offline data continuity and on-site alerts (e.g., pH threshold alarms).

Data Transmission:

Supports 4G/5G, Ethernet, and WiFi communication methods.

Securely uploads encrypted data to environmental monitoring platforms, integrating with cloud-based AI for trend analysis and predictions.

Environmental Platform Integration:

Aggregates data from multiple sites, generating water quality trend curves and spatial distribution maps.

Automatically produces regulatory reports to support environmental decision-making.

Advantages

Low Power Consumption: Supports solar power, ideal for long-term field operations.

High Reliability: Industrial-grade design, suitable for harsh outdoor conditions like high temperature and humidity.

Edge Intelligence: Local processing reduces cloud dependency and enhances response speed.

Flexible Compatibility: Open interfaces facilitate integration with various sensors and platforms.

Conclusion The ARM IoT Gateway demonstrates exceptional value in river and lake water quality monitoring. Its low power consumption and high reliability ensure stable long-term operation, while edge computing enhances data processing efficiency and response speed. The open interface design provides excellent scalability and compatibility. By seamlessly integrating with environmental platforms, this system not only enables real-time water quality monitoring but also provides data-driven decision support for water environment management, making it a key technical solution for advancing smart environmental protection.

Case Details

Seawater desalination is a critical solution to global water scarcity. Multi-Effect Distillation (MED) is widely used in coastal areas and islands for its high energy efficiency and operational stability. The MED system involves components such as evaporators, condensers, heaters, vacuum pumps, and circulation pumps, requiring precise control and real-time data acquisition to ensure efficiency and water quality.

Role of ARM Edge Logic Controller BL350 Series

The ARM edge logic controller serves as the core control unit of the MED system, integrating real-time data acquisition, edge computing, and remote monitoring to meet the demands of complex operational environments.

Data Acquisition and Monitoring

Temperature: Monitors temperatures in evaporators, condensers, and inlet/outlet pipelines.

Pressure/Vacuum: Tracks pressure conditions in evaporation and condensation chambers.

Flow: Measures circulation pump and product water flow rates.

Conductivity/TDS: Ensures product water quality through real-time monitoring.

Logic Control

Steam Flow Regulation: Automatically adjusts heating steam flow to optimize temperature gradients in evaporators.

Vacuum Pump Control: Maintains a low-pressure evaporation environment for enhanced efficiency.

Pump Logic Switching: Intelligently switches between circulation and feedwater pumps to prevent dry running.

Energy Efficiency Optimization

Utilizes edge computing to analyze thermal energy efficiency and dynamically adjust operating conditions.

Reduces steam and electricity consumption, improving overall system efficiency.

Safety and Alerts

Issues alerts for overtemperature, overpressure, or pump anomalies.

Employs local redundant logic during network disconnections to ensure safe operation.

Remote Operation and Maintenance

Supports 4G/Ethernet connectivity to SCADA systems or cloud platforms.

Provides VPN and secure encrypted channels for remote maintenance and parameter optimization.

Application Value

Enhanced System Stability: Ensures continuous and stable operation of the MED process.

Reduced Maintenance Costs: Remote operation minimizes the need for on-site personnel.

Improved Energy Efficiency: Edge computing optimizes thermal energy utilization, reducing energy consumption.

Assured Water Quality: Real-time conductivity/TDS monitoring guarantees high-quality freshwater output.

Summary

The ARM edge logic controller significantly enhances the efficiency and stability of MED systems through integrated data acquisition, logic control, energy optimization, and remote maintenance. Its edge computing capabilities reduce energy consumption, remote monitoring lowers operational costs, and real-time water quality monitoring ensures safe freshwater output, providing an efficient and reliable solution for seawater desalination.

Case Details

In modern manufacturing, energy consumption is a critical factor affecting business costs and sustainability. Traditional energy management relies on manual meter reading or periodic data collection, which suffers from delayed data, insufficient accuracy, and challenges in dynamic optimization. With advancements in IoT and AI technologies, AI edge gateways BL450 have become the core equipment for factory energy management, enabling enterprises to achieve cost savings and green production.

System Architecture and Functions

As the "neural hub" for energy optimization, AI edge gateways integrate the following key functions:

Real-Time Data Collection

Connects to devices such as electricity meters, heat meters, gas flow meters, and water meters via Modbus, CAN, RS485, or Ethernet interfaces.

Supports high-frequency sampling to collect multi-dimensional energy data, including electricity, water, and gas usage.

Edge Intelligence Analysis

Embedded AI models perform local data processing, such as anomaly detection, peak-valley load analysis, and predictive maintenance.

Reduces the need to upload large volumes of raw data, alleviating network bandwidth pressure.

Energy Optimization and Control

Dynamically adjusts operating parameters of equipment like air compressors, cooling towers, boilers, and lighting systems based on real-time analysis.

Achieves peak shaving and valley filling to enhance energy efficiency.

Secure Communication and Remote Management

Connects to cloud platforms via 4G/5G, Ethernet, or Wi-Fi for remote monitoring and report generation.

Employs VPN/SSL encryption to ensure industrial data security.

Application Value

Cost Savings: Optimizes energy allocation to reduce peak-hour electricity costs and overall energy expenses.

Predictive Maintenance: Monitors abnormal energy consumption in real time, issuing early warnings to prevent downtime losses.

Compliance and Sustainability: Provides energy consumption traceability and carbon emission statistics, supporting ESG and green factory goals.

Flexible Scalability: Supports multiple protocol integrations, easily adapting to various factory environments and equipment types.

Typical Application Scenarios

Injection Molding Workshop: Monitors electricity consumption of injection molding machines, identifies high-energy conditions, and optimizes production processes.

Steel Smelting: Tracks electric furnace loads, optimizes heating curves, and minimizes grid impact.

Textile Dyeing Plant: Collects steam and hot water usage data to improve boiler combustion efficiency.

Food Processing Plant: Monitors refrigeration system energy consumption and schedules cooling loads based on production plans.

Conclusion

AI edge gateways BL450 provide an efficient solution for factory energy management through real-time data collection, edge intelligence analysis, and dynamic optimization control. Their significant advantages in cost savings, predictive maintenance, regulatory compliance, and system scalability make them a key technology in driving smart manufacturing and green factory transformations. In the future, with further advancements in AI and IoT technologies, AI edge gateways will play an increasingly vital role in various industrial scenarios, helping enterprises achieve both economic and environmental benefits.

Case Details

The ARM Edge Gateway is the core of data acquisition and control in industrial settings, integrating the open-source OpenPLC control platform. It supports multiple programming methods such as Ladder Diagram (LD), Function Block Diagram (FBD), and Structured Text (ST), with built-in PID control algorithms. The gateway enables local closed-loop control and seamless communication with upper-level systems or cloud platforms via Ethernet, 4G, or WiFi, balancing real-time performance and flexibility.

System Architecture and Functions

Hardware Layer

Sensors: Collect signals such as temperature, pressure, liquid level, flow, and speed (supporting analog/digital inputs).

Actuators: Control devices like motors, valves, heaters, and pumps (supporting analog/relay outputs).

ARM Edge Gateway ARMxy series:

CPU: Cortex-A7/A53/A55, delivering robust performance.

Interfaces: optional RS485/RS121, Ethernet, IO, 4G/WiFi.

Functions: Real-time data acquisition from the field, supporting multiple industrial protocols.

Control Layer

OpenPLC Platform (Running on ARM Gateway):

Complies with IEC 61131-3 standards, supporting multiple programming languages.

Built-in PID function blocks, supporting proportional (Kp), integral (Ki), and derivative (Kd) parameter tuning, with flexible sampling cycle settings.

Supports multi-loop PID control, suitable for complex industrial scenarios.

Application Logic

Typical Control Loops:

Temperature Control: Sensor collects temperature → OpenPLC processes → Adjusts heater.

Pressure Control: Pressure sensor collects data → OpenPLC processes → Controls regulating valve.

Speed Control: Encoder feeds back speed → OpenPLC processes → Adjusts variable frequency drive.

Upper-Level Management

Local Management: Communicates with HMI/SCADA systems via Modbus TCP or MQTT protocols for real-time data monitoring.

Cloud Platform Integration: Supports historical data storage, remote parameter tuning, and trend analysis.

Alarm Mechanism: Automatically triggers alarms when control errors exceed set thresholds.

Typical Application Scenarios

Building HVAC: Precisely regulates room temperature.

Water Treatment Systems: Maintains constant water level or flow control.

Boilers/Kilns: Achieves closed-loop regulation of combustion temperature.

Motor Speed Regulation: Ensures constant speed or pressure operation.

Food & Beverage/Fermentation Industry: Accurately controls temperature and humidity to enhance production quality.

Solution Advantages

Open-Source and Customization: OpenPLC’s open-source nature provides high flexibility and low development costs.

Edge Computing Capability: The ARM gateway supports local computation, reducing dependency on cloud systems and minimizing network latency.

Strong Real-Time Performance: Local PID closed-loop control ensures fast response and high reliability.

High Scalability: Supports multiple industrial protocols (Modbus, OPC UA, MQTT), facilitating integration with cloud platforms or other systems.

Summary

This solution, centered on the ARM Edge Gateway and integrated with the OpenPLC open-source platform, builds an efficient, flexible, and cost-effective industrial control and data acquisition system. It achieves real-time performance through local PID closed-loop control and scalability through multi-protocol support and cloud integration, making it suitable for various industrial scenarios. Leveraging open-source features and edge computing capabilities, this solution offers robust customization and real-time control while reducing costs, making it an ideal choice for industrial automation.

Case Details

Photovoltaic power generation, a key form of clean energy, is widely adopted globally. The photovoltaic inverter, a core component, directly impacts power generation efficiency. Traditional monitoring methods struggle with data delays, low communication efficiency, and high maintenance costs, failing to meet real-time needs. ARM Edge gateways, with high performance, low power consumption, and flexible communication, offer an efficient solution for inverter monitoring.

Overview of ARM Edge Gateway BL335

The ARM Edge gateway BL335 is based on the ARM architecture, integrates a high-performance processor, and supports BLIoTLink protocol conversion software (such as Modbus and MQTT). It supports edge computing and data processing and is very suitable for deployment in photovoltaic power plants to meet real-time monitoring needs.

Needs for Photovoltaic Inverter Monitoring

Inverters require monitoring of parameters like voltage, current, power, and temperature. Traditional monitoring faces challenges:

Delayed data collection, hindering real-time monitoring.

Unstable networks in remote areas, causing data transmission issues.

Slow fault response due to reliance on manual diagnosis.

High maintenance costs for large-scale plants.

Applications of ARM Edge Gateway

Real-Time Data Collection and Processing

The ARM Edge gateway collects inverter data in real-time via RS485, 4G, WiFi, and other interfaces. Its built-in processor filters data and detects anomalies, reducing cloud data volume and improving efficiency.

Edge Computing and Fault Diagnosis

Supporting machine learning algorithms, the gateway analyzes data at the edge to predict faults (e.g., overheating, efficiency drops), reducing cloud dependency and speeding up responses.

Data Communication and Cloud Integration

The gateway uploads data via MQTT and other protocols, supporting offline data buffering to ensure data integrity. Bidirectional communication enables remote inverter control for efficient maintenance.

Low Power Consumption and High Reliability

With low-power design and industrial-grade protection, the ARM edge gateway adapts to harsh photovoltaic environments, ensuring long-term stable operation.

Case Study

A German photovoltaic power plant deployed the ARM BL335 Edge gateway to monitor hundreds of inverters. The gateway detected an abnormally high temperature in one inverter, identified it as a cooling system failure, and issued an alarm. Maintenance personnel remotely resolved the issue, avoiding downtime. Data uploaded to the cloud optimized scheduling, increasing power generation efficiency by 5%.

Technical Advantages

Real-Time Performance: Millisecond-level data processing for quick responses.

Intelligence: Edge computing enables fault prediction.

Efficient Communication: Multi-protocol support ensures reliable data transmission.

Cost Efficiency: Reduces cloud computing and maintenance costs.

High Adaptability: Low power consumption suits complex environments.

Conclusion

ARM Edge gateways BL335 enhance photovoltaic inverter monitoring with real-time data collection, edge computing, and efficient communication. As 5G and AI technologies advance, their role in the photovoltaic industry will grow, supporting the development of clean energy.

Case Details

With the advancement of Industry 4.0, the demand for real-time data acquisition and integration in manufacturing has grown significantly. Fanuc CNC (Computer Numerical Control) machines, as core equipment in industrial production, require data collection and monitoring to optimize processes and enhance efficiency. ARM Edge Gateways, with their low power consumption and high performance, are ideal for industrial IoT applications. By leveraging Node-RED’s flexible workflow design and the standardized OPC UA protocol, seamless data acquisition and monitoring from Fanuc CNC machines can be achieved. This article details how to use an ARM Edge Gateways with Node-RED to collect Fanuc CNC data and integrate it into OPC UA for monitoring.

System Architecture

The system architecture comprises the following components:

Fanuc CNC Machine: Provides operational data such as machining status, spindle speed, and tool position.

ARM Edge Gateway ARMxy series: Runs Node-RED for edge computing, handling data acquisition, processing, and protocol conversion.

Node-RED: A flow-based programming tool for designing data acquisition and processing workflows.

OPC UA Server: Offers a standardized data access interface for upper-level systems or monitoring software.

Monitoring System: Such as SCADA or MES, which retrieves data via OPC UA for analysis and visualization.

The data flow is: Fanuc CNC → ARM Gateway (Node-RED) → OPC UA Server → Monitoring System.

Implementation Steps

Configure the ARM Edge Gateway 

The ARM edge gateway (e.g., ARM Edge Gateway ARMxy or Raspberry Pi) requires a Linux operating system (e.g., Ubuntu or Raspbian). Key steps include:

Configure Fanuc CNC Data Interface

Fanuc CNC machines typically provide data access via the FOCAS (Fanuc Open CNC API Specification) protocol. Install the FOCAS library or related drivers on the gateway and ensure the machine’s Ethernet module is enabled.

Verify Connection: Test the connection to the CNC using a FOCAS client tool to retrieve machine status, parameters, and machining data.

Select Data Points: Choose data points to collect based on requirements, such as spindle speed (RPM), feed rate, or machining time.

Node-RED Workflow Design

Node-RED simplifies data acquisition and processing through a visual interface. Key configuration steps include:

Install Node-RED Nodes: Install the following node packages in Node-RED:

node-red-contrib-opcua: For OPC UA communication.

node-red-node-focas (or a custom FOCAS node): For Fanuc CNC communication.

npm install node-red-contrib-opcua

Design Workflow:

Input Node: Add a FOCAS input node, configuring the CNC’s IP address, port, and data points (e.g., spindle speed).

Processing Node: Use a Function node to format data, such as converting raw data into JSON format.

Output Node: Add an OPC UA server node, configure the OPC UA server address and namespace, and write formatted data to specified variables.

Configure OPC UA Server

Deploy an OPC UA server (e.g., open62541 or Node-OPCUA) on the ARM gateway or a separate server. Key steps include:

Install OPC UA Server

Configure Server: Set up the server endpoint (e.g., opc.tcp://:4840) and define the namespace and variable structure.

Map Data: Map Node-RED output data to the OPC UA server’s variable nodes, ensuring real-time updates.

Integration and Monitoring

Connect Monitoring System: Use an OPC UA-compatible SCADA or MES system (e.g., Ignition, WinCC), configure the client to connect to the OPC UA server, and subscribe to required variables.

Data Visualization: Create dashboards in the monitoring system to display real-time Fanuc CNC data, such as spindle speed and machining status.

Alarms and Analysis: Configure alarm rules (e.g., for abnormal spindle speed) and perform historical data analysis to optimize production.

Considerations

Network Security:

In industrial environments, configure firewalls and VPNs to ensure secure data transmission.

Enable authentication and encryption for the OPC UA server.

Performance Optimization:

Adjust Node-RED’s data acquisition frequency (e.g., once per second) to avoid impacting CNC performance.

Use the ARM gateway’s low-power mode for long-term operational stability.

Error Handling:

Add error-catching nodes in the Node-RED workflow to handle communication interruptions or data anomalies.

Configure logging for debugging and maintenance.

Conclusion

By combining an ARM Edge Gateway ARMxy series with Node-RED and the OPC UA protocol, efficient data acquisition and monitoring from Fanuc CNC machines can be achieved. This solution is cost-effective, flexible, and scalable, making it suitable for small and medium-sized manufacturing enterprises. In the future, integrating AI analytics modules can enable predictive maintenance, further advancing smart manufacturing.