Traditional alarms are good at detecting defined conditions. AI is useful when the interesting condition is a change in pattern.
Why thresholds struggle
A multivariate drift can be important long before any single tag crosses a hard limit.
The role of anomaly detection
Use AI to flag unusual patterns as an investigation signal, not automatically as a critical production alarm.
False positives matter
If the model constantly cries wolf, operators will ignore it. Operating mode, recipe, product and maintenance state need context.
Layer the system
Keep deterministic alarms for defined hazards and add AI-based advisory signals where they provide earlier or richer information.
Final takeaway
The engineering value comes from a measurable improvement, explicit boundaries and a design that remains understandable when the system is under pressure.