SIMATIC Anomaly Detection

Why Anomaly Detection?

Anomaly Detection is an AI-based analytics tool integrated with Industrial Edge. It identifies abnormal behavior in your production machines and plants to improve efficiency with deeper operational insights.

Prevent costly downtime from undetected abnormal behavior or damage in production. Anomaly Detection combines AI with Industrial Edge's flexibility and computing power for continuous, live anomaly detection. Use AI-powered analysis near data sources for early problem identification.

Detect anomalies quickly using its flexible, intuitive interface and single-click AI model creation. Choose between Anomaly Detection Basis to use Full Relationship Analysis (FRA) or Anomaly Detection Extension to use Deep Time Analysis (DTA).

Key benefits

Improve efficiency

Identify abnormal behavior in your processes using AI insights to improve operational efficiency. Leverage Industrial Edge for robust AI analytics to enhance productivity and performance.

Reduce downtime

Identify inefficiencies and bottlenecks faster to focus on critical areas. Improve line capacities and avoid costly downtime for reliable plant operations.

Harness more data

Access diverse data for deeper insights and precise decisions across your plant. Monitor industrial machines live and analyze historical data. Understand unexpected deviations, fluctuations or changed temporal behavior.

FEATURED CAPABILITIES

Early access to the right data

Industrial Edge compatibility

Combine AI vigilance with Industrial Edge power. Gain deeper data insight for higher efficiency and productivity. Detect anomalies using an intuitive interface.

Detection with low latency

Detect abnormal behavior, damage or vulnerabilities in production processes early for proactive intervention. Run AI and analytics close to data origination with low latency.

Two analysis methods

Choose two data analysis methods: Anomaly Detection Basis creates AI models with Full Relationship Analysis. Anomaly Detection Extension adds temporal behavior via Deep Time Analysis, including context.