Disruption Prediction in Fusion Devices through Feature Extraction and Logistic Regression
This work addresses disruption prediction for fusion energy research, but it is incremental as it applies standard methods to a competition dataset.
The paper tackled disruption prediction in fusion devices by extracting features from signals and applying logistic regression, achieving first place in the Multi-Machine Disruption Prediction Challenge.
This document describes an approach used in the Multi-Machine Disruption Prediction Challenge for Fusion Energy by ITU, a data science competition which ran from September to November 2023, on the online platform Zindi. The competition involved data from three fusion devices - C-Mod, HL-2A, and J-TEXT - with most of the training data coming from the last two, and the test data coming from the first one. Each device has multiple diagnostics and signals, and it turns out that a critical issue in this competition was to identify which signals, and especially which features from those signals, were most relevant to achieve accurate predictions. The approach described here is based on extracting features from signals, and then applying logistic regression on top of those features. Each signal is treated as a separate predictor and, in the end, a combination of such predictors achieved the first place on the leaderboard.