Third party funded individual grant
Acronym: CATCH
Start date : 01.01.2024
End date : 31.12.2024
This research project investigates the use of unsupervised change point analysis (CPA) methods for knowledge discovery in large-scale industrial time series datasets. Change point analysis offers a promising tool to mine large time series datasets by identifying abrupt and unexpected changes in time series data without requiring supervision. Relating the detected events between multiple signals offers valuable insight into the behavior of complex, dynamical systems enabling to mine events of interest often hidden in the vast amount of data produced by the systems. We apply the methods to sensor data from combined cycle power plants with thousands of simultaneous data streams.
This research project investigates the use of unsupervised change point analysis (CPA) methods for knowledge discovery in large-scale industrial time series datasets. Change point analysis offers a promising tool to mine large time series datasets by identifying abrupt and unexpected changes in time series data without requiring supervision.
The project systematically evaluates the applicability, feasibility, and parameterization of state-of-the-art CPA algorithms for real industrial data. Key activities include a comprehensive literature review, detailed data profiling and preparation, conceptual development of an evaluation framework, and quantitative comparison of multiple algorithms using representative use cases provided by industry partners. We extend the typical one-dimensional use-case, where CPA is applied to isolated signals, by comparing the extracted events of multiple signals to find relationships and to mine unexpected, isolated events.
In addition, a visualization prototype will be developed to support interpretation of detected changes and to demonstrate how results can be integrated into engineering workflows and monitoring processes.
By assessing whether detected change points correspond to meaningful anomalies/events in combined cycle power plants (steam generators specifically), the project aims to advance unsupervised data mining methods for complex industrial systems and provide practical guidance for their deployment in power plant monitoring.