Anomaly Detection
What Is Anomaly Detection?
Anomaly Detection is the process of identifying patterns or data points that deviate from normal behavior in real time. In the context of Edge AI, it enables intelligent devices to recognize unusual events—like equipment failures, security breaches, or sensor errors—without needing cloud dependency.
Why Is It Used?
Anomaly Detection is used to ensure reliability, safety, and operational efficiency in distributed systems. By detecting outliers directly at the edge, organizations can respond instantly to faults or threats, minimizing downtime and improving predictive maintenance accuracy.
How Is It Used?
In Edge AI systems, anomaly detection algorithms run locally on IoT or embedded devices. They analyze streaming data—such as vibration, temperature, or network activity—to detect abnormalities in milliseconds. This allows critical decisions to be made on-site, even with limited or no internet connectivity.
Types of Anomaly Detection
Statistical Models – Identify deviations based on predefined thresholds or data distributions.
Machine Learning Models – Use trained models (e.g., autoencoders, clustering) to spot complex or subtle anomalies.
Hybrid Approaches – Combine both methods for adaptive, high-accuracy detection at the edge.
Benefits of Anomaly Detection
Real-Time Insight: Detect anomalies instantly where data is generated.
Reduced Latency: Eliminates dependency on cloud processing.
Improved Security: Flags unusual activity locally before it escalates.
Operational Efficiency: Enables predictive maintenance and fault prevention.