Third party funded individual grant
Acronym: CATCH 2.0
Start date : 01.04.2025
End date : 31.03.2026
Building on previous successes in change point analysis, this project advances scalable methods for analyzing large, heterogeneous industrial time series data from power plants. By combining data-driven algorithms with expert knowledge and a platform-based deployment close to the data (cloud deployment), the project enables faster insights, improved anomaly interpretation, and practical decision support for complex thermodynamic systems
This follow-up research project builds on previous work that demonstrated the value of change point analysis (CPA) for knowledge discovery in heterogeneous industrial time series data. The project extends existing methods to support large-scale analysis of complex thermodynamic systems, with a particular focus on power plant data characterized by high dimensionality, heterogeneity, and varying data quality. Beyond detecting individual change points, the developed methods enable higher-level analyses such as sequence-based pattern discovery and the identification of related or decoupled changes across hundreds of time series.
A central objective of the project is to transition the previously developed prototypical algorithms into a scalable, platform-based environment close to the data sources (cloud deployment). In collaboration with Siemens Energy platform teams, the project aims to improve scalability, availability, and iteration speed, enabling broader evaluation across diverse real-world use cases. This setup allows domain experts to apply the methods without direct access to source code and supports faster feedback cycles for algorithmic refinement.
The research further focuses on systematic evaluation of the algorithms on additional industrial use cases, expert-guided parameter selection, and adaptation of methods to handle edge cases and varying data quality. By strengthening the connection between algorithmic development, scalable infrastructure, and expert-driven evaluation, the project advances the practical applicability of change point analysis as a decision-support tool for industrial time series analysis.