EPFL - Ecole Polytechnique Fédérale de Lausanne
Predictive maintenance is critical in complex industrial systems as it aims to reduce maintenance costs and prevent unplanned downtime by detecting anomalies before they lead to system failure. Anomaly detection is a central component of predictive maintenance and involves identifying deviations from normal system behavior.
However, constructing the graph from sensor recording data is often not trivial, because it is not clear how an edge between two time series should be defined. Transforming raw data into a graph representation is one of the key challenges faced in graph-based time series anomaly detection tasks.
Time series data is often used to monitor the health of machines and equipment, but traditional approaches to predictive maintenance rely on statistical methods or machine learning models that do not capture the complex relationships between different data points. Graph Neural Networks (GNNs) have emerged as a promising approach for anomaly detection in such cases.
GNNs encode graph structures by aggregating features of neighboring nodes, which allows them to capture both local and global structures of the graphs. By creating a graph structure from time series data, where each time series is represented as a node and edges capture interactions between the different time series, GNNs can analyze these relationships over time. In addition, GNNs can incorporate additional information about the time series as node and edge attributes, enabling them to capture nonlinear, large-scale, and heterogeneous relationships between time series. In addition, graphs can reveal fundamental insights into the system, which is valuable by itself, independent of an improvement in prediction quality.
Furthermore, complex industrial systems are monitored by a variety of different sensors, which collect large amounts of heterogeneous data. The increasing availability and diversity of data resources offer new possibilities for more efficient and reliable system condition monitoring. However, the heterogeneity of sensor data presents several challenges in graph construction and learning from sensory time series.
- Artificial Intelligence and Signal and Image Processing, Engineering and Technology
- Bachelor Thesis, Master Thesis, Semester Project
In this project the student will attempt to embed relevant physics priors in graph neural networks (GNNs) with the goal of developing an inverse modeling framework for the prediction of the underlying material model of a system, given the labeled input (load/excitation) and the output (mechanical response) data.
- Engineering and Technology
- Master Thesis, Semester Project
Develop a control system for bumper cars where there are no collisions, no matter how hard kids try!
Equip cars with local collision-avoidance controllers, smoothly overtaking control of the wheel/speed when cars are too close to each other.
- Electrical Engineering, Systems Theory and Control
- Master Thesis