Image Source: CEChina
Author | Vikas Maurya
Edge AI plays a significant role in enhancing the efficiency and flexibility of process control and automation operations, especially in remote sites or where cloud connectivity may be limited.
Artificial Intelligence (AI) and Machine Learning (ML) algorithms trained in the cloud can be executed on local computing devices at the network edge, enabling factories to securely process and analyze data in real-time at the point of production and use.
As a core element of digital transformation strategies, the transformative power of AI has permeated nearly every industry. From the perspective of process control and automation, AI demonstrates immense potential in supporting the analysis and processing of the vast amounts of data generated by thousands of interconnected devices, systems, and processes in modern industrial facilities.
Applying AI technology in typical industrial environments can help optimize the efficiency, reliability, and safety of control processes. It can reduce the need for human intervention in tedious or routine tasks, ultimately increasing operational uptime while lowering operational costs. In achieving these goals, AI also paves the way for the long-term vision of fully automated operations in factories.
AI algorithms driven by years of actual operational data can be trained through machine learning to identify trends and anomalies that human engineers may find difficult to detect. These insights can be used to preemptively alert to potential sensor failures or provide recommendations for fine-tuning specific processes to enhance energy efficiency.
The key to effective decision-making lies in ensuring timely access to accurate, relevant data and the ability to quickly analyze and interpret information. In the field of process control, it is this urgent need to “deeply mine the value of data” that focuses AI applications on the most valuable scenarios—namely, the same physical space where operational data is generated and utilized.
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Advantages of Edge AI
Edge Artificial Intelligence (Edge AI) typically refers to AI applications that execute tasks in real-time or near-real-time on connected devices. The data that provides the basis for AI engine decisions may be stored remotely in the cloud or reside close to the network edge where the device is located.
Although the origins of edge computing can be traced back to the turn of the millennium, deploying AI models at the network edge is a relatively new phenomenon. Thanks to advancements in CPU processing power and circuit miniaturization, compact hardware devices now possess data processing capabilities comparable to supercomputers from a decade ago.
High-speed 5G connectivity also allows for data collection from thousands or millions of Internet of Things (IoT) devices, providing data for increasingly complex AI/ML models hosted in the cloud or remote data centers. As these models are trained with more devices and more data, they become smarter, more accurate, and more reliable through continuous iteration.
Shifting AI processing power to the network edge brings several significant advantages. It greatly reduces the bandwidth requirements and associated costs of transmitting large amounts of data between field-based devices and the cloud. Executing applications locally, rather than at a remote location, also shortens system latency—the round-trip time between the data source and processing location. Whether in a self-driving car or performing robotic surgery on a patient across another continent, even a hundred milliseconds of input-output latency can have catastrophic consequences.
Deploying computing resources at the point of data generation and use can mitigate the cybersecurity risks that may arise from connecting sites to remote data centers over the internet. While private and public clouds have high inherent security, consistently keeping commercially sensitive data on-site ensures that unauthorized disclosures and scrutiny do not occur, thereby guaranteeing ultimate ownership and control over the data.
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Application Scenarios for Mission-Critical Tasks
Edge computing is also highly attractive in mission-critical applications because it does not rely on continuous internet connectivity to process data. This ensures high availability for applications that might otherwise be affected by network interruptions or connectivity issues at remote sites.
Given these benefits, it is not surprising that edge computing plays a transformative role in many industrial process control and automation environments. As an intelligent bridge between field devices and the cloud, it allows asset owners to collect more data from industrial IoT devices and control systems and utilize this data across a wide range of production processes.
All factory operators face the challenge of optimizing uptime, efficiency, safety, sustainability, and profitability. The key lies in extracting actionable insights in a timely manner from the vast amounts of operational, IT, and engineering data generated by thousands of sensors, subsystems, and other sources. This can be particularly challenging in remote areas far from mobile broadband coverage or in hard-to-reach environments, such as mines, chemical plants, offshore wind farms, or oil platforms. From a business perspective, AI analytics powered by edge and cloud computing hold the promise of helping industrial asset owners unlock real value from the approximately 80% of production data that currently goes untapped.
By definition, edge and cloud models are fundamentally different; however, they are widely viewed as complementary technologies. Combining the immediacy, security, and resilience of edge computing with the limitless scale and storage capacity of the cloud can bring many benefits to manufacturing enterprises.
For example, consider a process optimization model from a chemical processing plant. This model is supported by production data collected on-site from IIoT devices and hosted in the cloud, then executed in real-time on-site using edge AI. The results output by this model ensure that devices and systems can respond to environmental demands more quickly and accurately.
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A Powerful Tool for Empowering Industry
Edge AI can also serve as a powerful tool to empower other applications in manufacturing enterprises, such as condition-based asset health and performance monitoring. Data collected from sensors, actuators, and other devices at the network edge can serve as the foundation for condition-based monitoring. Trained AI algorithms can detect potential anomalies in the data signatures of connected devices or subsystems, cross-referencing behaviors with historical data from tens of thousands of similar devices. By sending automated notifications to factory personnel, it can provide early warnings of potential system failures that could otherwise impact factory or process performance and lead to costly unplanned downtime.
In the future, edge computing will have a transformative impact on a wide range of industrial automation applications. By combining industrial IoT technologies with AI-driven analytics, edge computing can complement cloud storage and applications, thereby helping the process industry fully realize the value of production data.
Key Concepts:
■ Edge AI reduces latency and bandwidth requirements, enabling secure, real-time data processing and analysis directly at industrial production sites.
■ AI enhances efficiency and safety by detecting anomalies in factory facilities and optimizing processes, reducing human intervention, and increasing uptime.
Consider:
How can Edge AI help your facility improve operations?
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▲ This article is from the March 2025 issue of CONTROL ENGINEERING China magazine, in the “Source of Technology” column: How Edge AI Empowers Process Manufacturing?
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