Traditional alarm systems only catch obvious threshold violations. Many of the most costly process upsets, quality deviations, and equipment...
Traditional alarm systems only catch obvious threshold violations. Many of the most costly process upsets, quality deviations, and equipment...
Overview
Traditional alarm systems only catch obvious threshold violations. Many of the most costly process upsets, quality deviations, and equipment failures are preceded by subtle, multivariate anomalies that conventional monitoring misses entirely. AhilyaSoft's Anomaly Detection platform uses unsupervised machine learning to learn the normal operating patterns of your process and sensitively detect any deviation: enabling early intervention hours or days before conventional alarms would trigger.
Methodology
Unsupervised ML models learn the normal multivariate operating patterns of your process: capturing complex interactions that define healthy operation.
Every data point is scored against the learned model in real time: quantifying how far current operation deviates from the expected normal pattern.
When anomaly scores exceed learned thresholds, context-rich alerts are generated: including which variables are contributing most to the anomaly.
Models continuously adapt to intentional process changes (new grades, operating modes) while remaining sensitive to unintentional anomalies.
What You Gain
Measurable results within weeks, not quarters.
Detect process anomalies hours to days before conventional alarms trigger
Reduce false alarm rate through multivariate pattern-based detection
Identify which variables are driving each anomaly for targeted investigation
No labeled failure data required: unsupervised learning from normal operation
Applicable to process, equipment, and quality anomaly detection
Deployed across industries where milliseconds and margins matter.
Applications
Power plant: early detection of boiler tube leak, condenser fouling, and efficiency degradation
Cement: kiln process anomaly detection for proactive intervention
Data centres: cooling system anomaly detection and hotspot prediction
Chemical process: reactor performance anomaly detection and catalyst deactivation monitoring
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