ARTIFICIAL INTELLIGENCE • INDUSTRIAL DATA • INTELLIGENT ANALYSIS
EOS AI Applications is an exploratory research and development area focused on using artificial intelligence to analyze data collected from industrial facilities.
The objective is to investigate how SCADA systems, data loggers and event recorders can be combined with modern artificial intelligence techniques to support operators, maintenance personnel and engineers in understanding complex industrial processes.
Modern industrial facilities continuously generate large amounts of operational data. SCADA systems collect real-time measurements, status information and alarms, while data loggers and event recorders preserve historical process information and detailed event sequences.
This information contains valuable knowledge about the behavior of equipment and processes. However, the volume and complexity of the data can make it difficult for operators and engineers to continuously examine all available information.
Artificial intelligence can potentially help by continuously analyzing this information, identifying unusual behavior, correlating events and alarms, detecting developing problems and presenting relevant information to the people responsible for operating and maintaining the facility.
The goal is not to replace operators or engineers. Instead, AI can be used as an additional analytical layer that helps people understand industrial data faster and make better-informed decisions.
SCADA systems provide continuously updated information about process variables, equipment states, alarms, setpoints and operating conditions.
Long-term historical data collected by data loggers can be used to understand normal operating behavior, identify trends and investigate changes in equipment performance.
Event recorders preserve sequences of alarms, status changes and system events. This information can be particularly valuable when investigating abnormal operating conditions.
An intelligent assistant that analyzes current alarms, process conditions and recent events and provides operators with relevant information, possible causes and recommended investigation steps.
AI can analyze equipment behavior, historical events and maintenance records to help maintenance personnel identify possible equipment problems and prioritize inspections.
Continuous analysis of process data can identify unusual patterns and deviations from normal operating behavior before they become obvious through conventional alarm systems.
AI can analyze large numbers of simultaneous alarms, identify related alarm groups and help distinguish potentially important events from secondary or consequential alarms.
Events occurring across different systems and equipment can be correlated according to their timing and process relationships to help identify possible sequences and common causes.
Historical measurements and equipment behavior can be analyzed to identify patterns associated with degradation and potentially provide early warnings of developing failures.
AI models can continuously evaluate multiple process variables and identify combinations of conditions that may indicate an emerging problem.
Historical alarms, events and process variables can be analyzed together to investigate the sequence of conditions leading to an abnormal operating state.
One of the most promising areas for industrial AI is the continuous analysis of historical and real-time data. Instead of waiting for an operator to investigate a problem, an AI-based system could continuously examine the behavior of the plant.
The system could compare current measurements with historical operating patterns, examine relationships between multiple variables and identify combinations of conditions that appear unusual.
For example, a temperature value may remain within its normal alarm limits while gradually changing in a way that is unusual for a particular operating condition. An AI-based analysis system could potentially recognize this deviation even though no conventional alarm has been triggered.
Similar analysis could be applied to vibration, pressure, temperature, flow, electrical measurements, equipment states, communication quality and other continuously recorded data.
Conventional SCADA systems report alarms according to predefined limits and rules. This is essential for safe and reliable operation, but large industrial systems can generate many alarms during disturbances.
An AI-based alarm analysis layer could examine the sequence, timing and relationships between alarms and process variables. Instead of simply presenting hundreds of alarms, the system could help answer questions such as:
One possible future application is an AI-powered operational assistant connected to SCADA, event recorder and historical data systems.
The operator could ask questions using natural language, such as:
"What caused the increase in turbine temperature?"
"Have we experienced a similar alarm sequence before?"
"Which equipment should the maintenance team check first?"
"What changed during the last 30 minutes?"
The system could search historical data, analyze recent events and alarms, correlate relevant measurements and present the results in a form that is easier for the operator or engineer to understand.
Equipment failures are often preceded by changes in operating behavior. These changes may appear as gradual deviations, increased variability, changes in relationships between measurements or unusual combinations of process conditions.
AI-based predictive maintenance applications can investigate historical equipment data to identify patterns associated with degradation or previous failures.
Potential applications include pumps, motors, generators, transformers, turbines, fans, compressors, cooling systems, valves and other industrial equipment for which sufficient historical data is available.
AI applications for industrial systems require more than simply connecting a language model to SCADA data. The reliability, timing, quality and context of industrial information must also be considered.
AI applications described on this page are currently research and development topics rather than finished EOS software products.
Initial studies are focused on understanding how industrial data collected by SCADA systems, data loggers and event recorders can be used effectively with artificial intelligence technologies.
The objective is to develop practical approaches that can provide useful information to operators, maintenance personnel and engineers while keeping humans in control of operational decisions.
Industrial facilities already contain large amounts of valuable operational information. SCADA systems, data loggers and event recorders continuously collect information that can potentially be transformed into higher-level knowledge using artificial intelligence.
The long-term objective of this work is to investigate practical AI applications that can help industrial teams understand their systems, identify abnormal behavior, investigate events and improve operational awareness.
EOS Industrial Software Hub will share research results, technical information, experimental applications and practical examples as this work develops.
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