AI in alarm management: How AI is changing HMI SCADA

Industrial alarm systems generate enormous amounts of data. Every alarm, event, and process change adds to a history that can help engineering teams understand how a system is actually performing. The challenge is making sense of it.

Traditional alarm analysis can reveal a great deal about system performance, but finding deeper relationships across thousands of alarm events can still be difficult. Which alarms repeatedly occur together? What tends to happen before an alarm flood? Where did a cascade begin? Which recurring problems deserve attention first?

Artificial intelligence (AI) and advanced analytics are changing what engineering teams can learn from that history and how quickly they can act on it. AI can analyze alarm behavior at scale, helping teams uncover deeper patterns, investigate likely root causes, and determine where changes could improve alarm performance. 

Historical alarm and event data can already help teams identify frequent alarms, alarm floods, chattering alarms, standing alarms, and other performance issues. These types of analysis help engineers understand where alarm problems exist and where to focus their attention.

AI adds another layer by examining relationships across alarms, events, and process conditions. Rather than looking only at individual alarms or performance metrics, teams can investigate sequences and connections within the broader history of the system. This can provide additional context for understanding how an alarm problem developed and what may be contributing to it.

Alarm overload is not the result of a single poorly configured alarm. It can be engineered into a system from the start or develop as alarm noise accumulates over time, which can make it difficult to know where an improvement effort will have the greatest effect.

Consider an alarm flood in which dozens of alarms occur within a short period. The first alarm an operator sees may not be the condition that triggered the cascade. Looking across historical alarm behavior may reveal that one alarm consistently occurs before several others, that the same sequence has happened before, or that multiple alarms can be traced back to a common process condition.

AI-driven analysis can surface those relationships and help teams trace a flood back toward its likely source. It can also expose recurring alarm issues and inefficiencies buried within large volumes of historical data, helping engineers prioritize the problems most likely to improve alarm performance.

For engineering teams, one of the most practical benefits of AI may be accelerating the investigation of complex alarm histories. AI-generated insights can give engineers a stronger starting point for troubleshooting and improvement.

For example, an alarm that appears to occur unpredictably several times a day might consistently follow the startup of a particular pump. Identifying that relationship gives the engineer a more focused place to investigate, such as the process behavior during startup, the control logic, or the alarm configuration.

Process knowledge and engineering expertise remain essential. Alarm behavior exists within the context of the process, control strategy, operator workflows, operational priorities, and risk. Bringing that expertise together with AI creates the potential for faster diagnosis, stronger recommendations, and more effective action.

Velotic is bringing AI-driven alarm analysis into Proficy HMI SCADA through new Alarm Intelligence capabilities designed to build on existing iFIX and CIMPLICITY environments.

Alarm Intelligence uses analytics and AI to help teams analyze historical alarm and event behavior, detect complex patterns and relationships, examine connections among alarms and process conditions, investigate likely root causes, and prioritize opportunities for alarm rationalization.

AI-driven correlation analysis can help engineers trace how alarms are connected and investigate where an underlying issue may have begun. An Alarm Assistant allows engineers to explore alarm information using natural language. Instead of starting with a predefined report or knowing exactly where to look, engineers can ask questions about alarm behavior and use the responses to guide further investigation.

Alarm history has long helped engineering teams understand system performance. Analytics expanded teams' ability to measure performance, identify trends, and uncover recurring issues. AI opens the door to something more: systems that can find deeper relationships across large volumes of operational data, help diagnose why problems are occurring, and increasingly recommend how to address them.

As AI becomes more deeply integrated into HMI SCADA workflows, those capabilities can continue to advance. Engineering teams can move faster from detection to diagnosis and from diagnosis to action, with AI playing a growing role in analyzing conditions, recommending next steps, and potentially supporting automated responses where appropriate.