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✎ Introduction
Manufacturing companies are increasingly focusing on the value of edge-hosted artificial intelligence in enhancing process control and automation operational efficiency and resilience, especially in remote sites or places where cloud connectivity cannot function.
Author: Vikas Maurya, Source: Control Engineering China, published with permission from Industrial 4.0.
Today, AI and machine learning algorithms trained on cloud-hosted datasets can execute on local computing devices at the network edge, allowing factories to securely and in real-time process and analyze data at the point of production and use.
As a core element of digital strategy, few industries are untouched by the transformative power of artificial intelligence. Moreover, from the perspective of process control and automation, AI has immense potential to analyze and manage the vast datasets generated by thousands of interconnected devices, systems, and processes within modern industrial facilities.
In a typical industrial environment, AI can help optimize the efficiency, reliability, and safety of control processes. It can also help reduce the need for human intervention in heavy or routine tasks, ultimately contributing to increased factory uptime while lowering operational costs. In achieving these goals, AI also paves the way for the long-term objective of fully automated factory operations.
AI algorithms are supported by years of accumulated operational data and can be trained using machine learning to discover trends and anomalies that human engineers might not notice. These insights can provide early warnings of impending sensor failures or suggest how to fine-tune specific processes to make them more energy-efficient.
Effective decision-making depends on ensuring timely access to accurate, relevant data and the ability to quickly analyze and interpret that information. In process control environments, it is this necessity of “doing more with data” that has drawn attention to where AI is most valuable—namely, in the production and utilization of operational data.
01Edge Artificial Intelligence “Edge artificial intelligence” refers to the application of AI on connected devices to perform tasks in real-time or near real-time. The data that informs the decision-making process of AI engines can be remotely hosted in the cloud or it can be located at the network edge, very close to the devices themselves.While the origins of edge computing can be traced back to around 2000, deploying AI models at the network edge is a relatively new phenomenon. Advances in CPU power and miniaturization of circuits have provided digital processing capabilities for compact hardware devices that were once thought to be the domain of supercomputers a decade ago. Similarly, high-speed 5G connectivity allows data to be collected 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.Shifting AI computing capabilities to the network edge has several benefits. First, it can significantly reduce the bandwidth requirements and associated costs of transmitting large amounts of data from field devices to the cloud. Executing applications locally rather than at a distant location can also shorten system latency—the round-trip time between the data source and processing location. Whether you’re sitting in a self-driving car or performing robotic surgery on a patient across the continent, even a 100-millisecond delay between system input and output can have catastrophic consequences.Concentrating computing resources at the point of data generation and consumption can mitigate potential cybersecurity risks associated with connecting sites to remote data centers over the internet. While private and public clouds offer a high degree of inherent security, being able to always keep commercially sensitive data local ensures it is not subject to unauthorized leaks and scrutiny, thereby ensuring ultimate ownership and control over the data.02The Role of Edge Computing in Mission-Critical Applications
Similarly, edge computing is also appealing in mission-critical applications because it does not rely on a continuous internet connection to process data. This ensures high availability of applications that might otherwise be affected by network interruptions or poorly connected remote sites.
Given these advantages, it is no surprise 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 fully leverage the increasingly large amounts of data collected from industrial IoT devices and control systems.
Every factory operator faces the challenge of optimizing factory uptime, efficiency, safety, sustainability, and profitability. The key is to extract 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 is particularly challenging in remote or hard-to-reach environments, such as mines, chemical processing plants, offshore wind farms, or oil platforms, where mobile broadband coverage is absent. From a business perspective, AI analytics supported by edge and cloud computing help unlock real value from the approximately 80% of production data that factories are currently not utilizing.
By definition, edge and cloud computing models are fundamentally different. However, they are widely viewed as complementary technologies that combine the immediacy, security, and resilience of edge computing with the limitless scale and storage capacity of cloud computing.
A typical example is a process optimization model for a chemical plant. This model is supported by large-scale production data collected from on-site industrial IoT devices and can then be executed in real-time on-site using edge AI. The output of this model ensures that devices and systems can respond to their environmental demands faster and more accurately.
03Powerful Edge Intelligence Tools
Edge artificial intelligence can also serve as a powerful tool to support other use cases for industrial equipment owners, such as condition-based asset health and performance monitoring. Instrumentation data collected from sensors, actuators, and other devices at the network edge can serve as the basis for condition-based monitoring. Well-trained AI algorithms can detect potential anomalies in the data characteristics of connected devices or subsystems and cross-reference their behavior with historical data from thousands of other similar devices. By sending automated alerts to factory personnel, it can provide early warnings of potential system failures that could otherwise impact factory or process performance, leading to costly unplanned downtime.
In the future, edge computing will have a transformative impact on numerous industrial automation applications. By combining industrial IoT technologies with AI-driven analytics, edge computing can serve as a complement to cloud-hosted storage and applications, thereby unlocking more value from production data in process industries.
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