Anomaly Detection

Definition

Anomaly Detection is a data analysis technique that identifies rare items, events, or observations that deviate significantly from the majority of normal data patterns. In manufacturing environments, it automatically detects situations outside the normal range such as equipment failures, product defects, and process abnormalities, enabling early response.

Applications in Manufacturing

Predictive Maintenance

• Detect equipment failure signs through vibration, temperature, and pressure sensor data analysis
• Learn normal operation patterns and alert on deviations in real-time
• Prevent unexpected downtime and reduce maintenance costs

Quality Control

• Automatic defect detection in vision inspection systems
• Monitor abnormal variations in process parameters (temperature, speed, pressure)
• Identify micro-defects to improve yield rates

Production Process Optimization

• Detect abnormal patterns in energy consumption and production speed
• Early identification of delivery delay risks in supply chain data

Key Points

Implementation Methods: Various AI algorithms including statistical methods (Z-score, IQR), machine learning (Isolation Forest, Autoencoder), and deep learning (LSTM, VAE)

Real Cases: Wafer defect detection in semiconductor manufacturing, real-time welding quality monitoring in automotive parts, and temperature anomaly detection in steel processes are representative examples.