Context Presentation
Anomaly detection in real fluid distribution applications is a difficult task, especially, when we seek to accurately detect different types of anomalies and possible sensor failures. Resolving this problem is increasingly important in building management and supervision applications for analysis and supervision. Our case study is based on a real context: sensor data from the SGE (Rangueil campus management and operation service in Toulouse).
We propose CoRP” Composition of Remarkable Points” a configurable approach based on pattern modelling, for the simultaneous detection of multiple anomalies. CoRP evaluates a set of patterns that are defined by users, in order to tag the remarkable points using labels, then detects among them the anomalies by composition of labels. CoRP is evaluated on real datasets of SGE and on state of the art datasets and is compared to classical approaches.
Figure 1: « Anomaly Detection in Sensor Networks »
Scientific Goals
- Detect different types of anomalies observed in real deployment
- Improve the supervision of sensor networks
- Use learning methods for anomaly detection on static and continuous data
Keywords
neOCampus, Sensor Data, Univariate Time Series, Anomaly Detection, Pattern-based Method
Contacts
Ines.ben-kraiem@irit.frOlivier.teste@irit.fr, andre.peninou@irit.fr
