Edge Computing Technology Applications in Industrial Automation: A PLC-Based Research Perspective
Abstract
In the context of Industry 4.0, industrial automation systems are placing higher demands on real-time performance, data processing capabilities, and intelligent decision-making. Edge computing processes data at on-site nodes, providing low-latency, highly reliable data channels for high-frequency control and intelligent optimization. This paper examines the technical implementation and system architecture of edge computing in real-time control, predictive maintenance, and process optimization, focusing on PLCs (Programmable Logic Controllers) and their analog modules (such as the GE IC693ALG221).
1. Introduction
Traditional industrial automation relies on centralized PLC and SCADA systems, but these systems face bottlenecks in large-scale data acquisition, complex algorithm computation, and real-time closed-loop control:
Communication latency: Centralized control systems have limited capabilities for high-speed sampling and feedback processing.
Bandwidth pressure: Industrial sensors generate large amounts of analog data that need to be transmitted remotely, increasing network load.
System reliability risk: A central server failure can cause an entire production line to shut down.
Edge computing effectively addresses these issues by deploying computing nodes near data sources to perform local data processing, AI inference, and control strategy execution, thereby enhancing the intelligence of PLC systems.
2. The Role of the GE IC693ALG221 Module in Edge Computing Systems
2.1 High-Precision Data Acquisition
The IC693ALG221 supports 16-bit analog input/output resolution, enabling real-time acquisition of key process parameters such as temperature, pressure, and flow. This high-precision acquisition ensures reliable data quality when edge nodes run AI algorithms, reducing error accumulation in predictive maintenance and optimization algorithms.
2.2 Fast Response
The module offers fast response time and supports multi-channel parallel sampling, meeting the real-time requirements of high-frequency control and closed-loop optimization at the edge. This is particularly important for high-speed production lines with control algorithm execution cycles of less than 50ms.
2.3 Industrial-Grade Anti-Interference Capability
The IC693ALG221 features industrial-grade EMI immunity, ensuring signal stability in environments with strong electromagnetic interference. This ensures the accuracy of sensor data processed by edge nodes, providing trusted input for AI reasoning and control decisions.
3. Edge Computing Architecture and Technology Implementation
3.1 System Architecture
A typical edge computing industrial control system includes:
PLC + analog module layer: collects field sensor data and executes primary control logic.
Edge computing nodes: deploy microprocessors or embedded AI chips to locally run predictive maintenance, process optimization, and energy analysis algorithms.
Cloud-based analytics layer (optional): used for historical data storage, deep learning model training, and cross-production line optimization.
The data flow is as follows:
Sensor → IC693ALG221 module → Edge node → PLC output → Actuator
Local AI analysis results can directly adjust PLC control strategies, achieving closed-loop optimization.
3.2 Algorithms and Data Processing
Common algorithms used at edge nodes include:
Predictive maintenance: identifies equipment abnormal trends based on time series analysis (ARIMA) and LSTM neural networks.
Process optimization: utilizes reinforcement learning (RL) or model predictive control (MPC) to dynamically adjust parameters to optimize yield and quality.
Energy Management: Real-time analysis of motor load and production rhythm reduces energy consumption through local control.
Algorithm execution relies on high-precision analog signals acquired by the module. The IC693ALG221's multi-channel parallel sampling ensures data synchronization, meeting the continuous data requirements of AI models.
4. Technical Challenges
Limited Edge Node Computing Resources: AI inference and control must be implemented in a low-power, CPU/GPU-limited environment.
Data Synchronization and Consistency: Multi-channel acquisition and multi-node collaborative control require addressing timestamp synchronization and data consistency issues.
Industrial Network Security: Edge nodes are exposed at the edge of the network, requiring enhanced encrypted communication, firewalls, and access control.
Module Compatibility: The communication protocol between PLCs and edge nodes needs to be standardized to ensure system scalability.
5. Prospects and Development Directions
Deep Integration of PLCs and Edge Nodes: Future modular "smart I/O" may integrate edge computing capabilities directly on the PLC side, simplifying system architecture.
AI Model Lightweighting and Collaborative Computing: Edge-cloud collaboration and model compression technologies are used to improve the operational efficiency of AI algorithms at field nodes.
Optimizing Industrial Network and Security Standards: Communication protocols and network security systems for industrial edge computing will be further improved to ensure system stability.
6. Conclusion
Edge computing, through local data processing and intelligent reasoning, provides low-latency, high-reliability, and distributed intelligent control capabilities for industrial automation systems. Combined with the high-precision, multi-channel acquisition, and industrial-grade reliability of the GE IC693ALG221 analog module, PLC systems can implement intelligent control functions such as real-time optimization, predictive maintenance, and energy management. In the future, the deep integration of edge computing and PLC will become a key direction for the development of industrial intelligence, providing technical support for efficient and sustainable production.
32D Guomao Building, No.388, Hubin South Road, Siming District, Xiamen, Fujian, China
