What is anomaly detection?
Anomaly detection is the process of identifying data points, events, or patterns that deviate significantly from normal behavior. In industrial applications, it detects equipment degradation, security breaches, and process failures before they cause damage.
Types of Anomalies
- Point anomalies — a single data point is abnormal (e.g., a sudden temperature spike)
- Contextual anomalies — a data point is abnormal in context (e.g., high load during low-demand period)
- Collective anomalies — a group of data points is abnormal together (e.g., a gradual drift across multiple sensors)
Methods
- Statistical — deviation from statistical distribution
- Machine learning — autoencoders, isolation forests, one-class SVM
- Deep learning — LSTM autoencoders for time-series data
- Hybrid — combining multiple methods for robustness
VLTHRLAB's Approach
VLTHRLAB uses LSTM autoencoders for time-series anomaly detection. The model learns normal equipment behavior and flags deviations via reconstruction error. This approach achieved AUC up to 1.000 on 8 benchmark datasets.
Evidence
VERIFIED
AUC up to 1.000 on 8 datasets
Source: vlthrlab.app/research/mad-system.html
Source: vlthrlab.app/research/mad-system.html