Artificial intelligence (AI) and machine learning are rapidly changing many industries. While these two terms are often – and incorrectly – used interchangeably, machine learning is a subclass of artificial intelligence. Put simply, AI is an overarching concept of creating intelligent machines that can perform tasks that generally require human intelligence, including problem-solving, pattern recognition, natural language processing, and decision-making. Machine learning is a specific approach within the broader field of AI that focuses on training machines to learn from data, and to make improvements in processes without being explicitly programmed to do so.
Industrial automation has been one of the industries most affected by the rapid acceleration of AI and machine learning technology. Broadly, industrial automation refers to the process of using technology and machinery to carry out industrial tasks and processes with little to no human interaction. Ultimately, the end goal of all industrial automation – whether in the context of process control, or manufacturing, or OEM – is to increase productivity and efficiency, while at the same time eliminating human-centric variables and the need for humans to perform dangerous industrial tasks.
Although industrial automation has been one of the major drivers of Industry 4.0, the recent advances in AI and machine learning technology represent a significant shift away from traditional automation processes and methodology. This shift is quite clear in several areas:
Traditional Rule-Based Automation vs. Adaptive Automation
Traditional automation systems rely on rule-based programming. In this framework, machinery and technology are programmed to respond to a set of predefined if-then-else type rules. Engineers and developers explicitly define rules and instructions for the machinery and technology to follow. Rule-based automation is effective for simple processes, but this type of system has limited adaptability when presented with unforeseen scenarios and can struggle in complex, changing environments.
Adaptive automation systems, specifically those that utilize AI and machine learning, can learn from data, and adapt to rapidly changing conditions. Adaptive systems can make decisions based on patterns and trends in the data without being explicitly programmed for every possible scenario.
Programming, System Design, and Flexibility
Traditional automation systems require a large amount of upfront programming and coding. All the if-then-else type rules, noted above, must be explicitly programmed into the system. Essentially, every potential action and response from the automated machinery must be pre-defined. This approach is acceptable for very simple systems with one or two possible outcomes. However, as systems become more complex, the amount of coding and programming increases nearly exponentially. Further, the functionality of traditional automation systems remains static unless they are manually reprogrammed. This rigidity has the overall effect of limiting the flexibility and adaptability of traditional automation systems.
Alternatively, automation systems powered by AI and machine learning can rapidly adapt to changes, often with little or no human input. Adaptive automation systems are driven by machine learning algorithms that enable continuous adaptation and process optimization without the requirement of reprogramming or reengineering.
Perhaps the most important difference between traditional rule-based automation and adaptive automation lies in how each respond to novel or complex problems and edge cases. As noted above, traditional automation systems only perform tasks or processes when given explicit rules or commands. As the complexity of a system increases, traditional systems struggle to adapt to dynamic environments. These traditional systems are ‘dumb’ in the sense that they only react based on defined logic.
Adaptive automation systems powered by AI and machine learning can impart a measure of device intelligence into these formerly ‘dumb’ systems and devices by making use of data gathered over time. Whereas traditional systems are reactive and follow predefined logic, adaptive systems can refine and optimize processes and react to unforeseen scenarios. By analyzing the data and learning patterns, adaptive systems can handle complex decision-making. Through analysis of historical data, these systems can anticipate and predict component or subsystem issues which enables proactive maintenance. According to the International Society of Automation (ISA), adopting a preventative maintenance approach can provide savings from 8% to 12% over reactive maintenance, and can reduce equipment and machinery downtime by 35% to 45%.
How AI & Machine Learning Are Changing Industrial Automation
The use of AI and machine learning is dramatically changing the industrial automation industry. The traditional approach to automation, which relies on if-then-else, rule-based programming, is limited in terms of flexibility and its ability to respond to novel scenarios that fall outside of explicitly defined parameters. However, the shift towards adaptive automation systems, powered by AI and machine learning, brings increased flexibility, predictive capabilities, and the ability to handle complex decision-making, contributing to more efficient and responsive industrial processes.
Several different components make up an industrial automation system utilizing AI and machine learning. These include sensors or other data gathering and data acquisition devices, a data storage and processing component, AI and machine learning models and algorithms, control systems that translate the AI and machine learning decisions into actions, a human-machine interface, and a communications network.
Sealevel develops and manufactures solutions for a variety of these component nodes. For example, a manufacturer of process automation control systems for the pharmaceutical industry relies on Sealevel products to address both data acquisition and to improve their communications network. One of the world’s leading producers of glass bottles has incorporated rugged and temperature resistant Sealevel products into their data acquisition equipment, control systems, and communications networks. And a provider of Advanced Driver Assistance Systems (ADAS) technology has partnered with Sealevel to improve their data acquisition devices and communications networks. Sealevel delivers proven, integration-ready products and customized solutions for advanced systems, achieved by leveraging over 35 years of engineering and manufacturing experience. From data acquisition devices to manufacturing automation to complete system automation and control, Sealevel’s team delivers unparalleled performance while meeting industry-specific certifications and requirements
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