CLIFFGATECLIFFGATE · Industrial AI
Industrial AI: Early warning for dangerous situations
Anomaly detection using time-series signals and operational context
Бізнес-задача
Dangerous situations often emerge gradually: abnormal vibration, temperature drift, pressure instability, acoustic changes, or repeated minor alarms. Traditional alarm systems can generate too many false positives and still miss complex patterns.
Технічний підхід
- —Use LSTM and anomaly-detection models for temperature, vibration, acoustic signals, pressure, flow, and equipment-state data.
- —Learn normal behavior by asset, operating regime, product type, load, and environmental context.
- —Classify anomaly severity and explain which signals contributed to the alert.
- —Continuously improve false-positive and false-negative rates through operator feedback.
- —Integrate alerts with HSE procedures, incident journals, and escalation chains.
Бізнес-результат
- —Earlier detection of unsafe conditions.
- —Fewer unnecessary alarms and better operator trust.
- —Reduced probability of major equipment failure or process incident.
- —Structured HSE evidence for management review.
- —Continuous learning from every incident, near miss, and confirmed abnormal situation.