The Inherent Flaws of Threshold Alerting
Threshold alerting logic is simple: trigger when a value exceeds a limit. But in industrial settings, this simple logic creates three fatal problems:
1. Alert Storms
Equipment start/stop, door openings, grid fluctuations — these brief disturbances cause dozens of points to exceed limits simultaneously, flooding the operator's phone. Among them, perhaps only 1–2 actually need immediate attention.
2. Alert Fatigue
When 60 out of 100 alerts need no action, the instinctive human response is "ignore them all." Over time, truly dangerous alerts are also buried.
3. Slow Degradation Goes Unseen
Temperature rising 0.3°C per day, current increasing 1A per month — these "boiling frog" changes won't trigger traditional thresholds until it's too late, by which time equipment may be severely damaged.
How AI Time-Series Anomaly Detection Works
Unlike traditional thresholds, AI methods ask: "Compared to its own history, is this device normal today?"
**Three core capabilities:**
| Method | Principle | Problem Solved |
|---|---|---|
| Dynamic Baseline | Learns each device's own historical operating patterns | Slow drift, hidden degradation |
| Time-Window Merging | Same-type alerts merged into one within a set time window | Alert storms, duplicate pushes |
| Delayed Confirmation | Brief fluctuations pass through a filter window before triggering | Door openings, start/stop transient interference |
Real Results Comparison
In a pilot factory's air compressor monitoring:
**A good alert system doesn't tell you more — it shows you only what matters.**