What is anomaly detection?
Anomaly detection is the process of identifying data points, events, or patterns that differ significantly from normal behavior. In industrial applications, it detects equipment wear, security breaches, and process failures before they cause damage.
Types of Issues
- Point issues — a single data point is unusual (e.g., a sudden temperature spike)
- Contextual issues — a data point is unusual in context (e.g., high load during low-demand period)
- Collective issues — a group of data points is unusual together (e.g., a gradual drift across multiple sensors)
Methods
- Statistical — deviation from normal patterns
- Machine learning — autoencoders, isolation forests, one-class SVM
- Deep learning — LSTM autoencoders for time-series data
- Hybrid — combining multiple methods for better results
VLTHRLAB's Approach
VLTHRLAB uses LSTM autoencoders for time-series anomaly detection. The model learns normal equipment behavior and flags changes 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