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Title: An application of survival analysis to disruption prediction via Random Forests

Abstract

One of the most pressing challenges facing the fusion community is adequately mitigating or, even better, avoiding disruptions of tokamak plasmas. However, before this can be done, disruptions must first be predicted with sufficient warning time to actuate a response. The established field of survival analysis provides a convenient statistical framework for time-to-event (i.e. time-to-disruption) studies. This paper demonstrates the integration of an existing disruption prediction machine learning algorithm with the Kaplan-Meier estimator of survival probability. Specifically discussed are the implied warning times from binary classification of disruption databases and the interpretation of output signals from Random Forest algorithms trained and tested on these databases. This survival analysis approach is applied to both smooth and noisy test data to highlight important features of the survival and hazard functions. In addition, this method is applied to three Alcator C-Mod plasma discharges and compared to a threshold-based scheme for triggering alarms. In one case, both techniques successfully predict the disruption; although, in another, neither warns of the impending disruption with enough time to mitigate. For the final discharge, the survival analysis approach could avoid the false alarm triggered by the threshold method. Limitations of this analysis and opportunities for future work aremore » also presented.« less

Authors:
; ; ; ;
Publication Date:
DOE Contract Number:  
FC02-99ER54512; SC0014664
Research Org.:
Massachusetts Inst. of Technology (MIT), Cambridge, MA (United States). Plasma Science and Fusion Center
Sponsoring Org.:
USDOE Office of Science (SC), Fusion Energy Sciences (FES)
Subject:
70 PLASMA PHYSICS AND FUSION TECHNOLOGY
OSTI Identifier:
1881445
DOI:
https://doi.org/10.7910/DVN/GW6HWH

Citation Formats

Tinguely, R. A., Montes, K. J., Rea, C., Sweeney, R., and Granetz, R. S. An application of survival analysis to disruption prediction via Random Forests. United States: N. p., 2019. Web. doi:10.7910/DVN/GW6HWH.
Tinguely, R. A., Montes, K. J., Rea, C., Sweeney, R., & Granetz, R. S. An application of survival analysis to disruption prediction via Random Forests. United States. doi:https://doi.org/10.7910/DVN/GW6HWH
Tinguely, R. A., Montes, K. J., Rea, C., Sweeney, R., and Granetz, R. S. 2019. "An application of survival analysis to disruption prediction via Random Forests". United States. doi:https://doi.org/10.7910/DVN/GW6HWH. https://www.osti.gov/servlets/purl/1881445. Pub date:Tue Nov 19 00:00:00 EST 2019
@article{osti_1881445,
title = {An application of survival analysis to disruption prediction via Random Forests},
author = {Tinguely, R. A. and Montes, K. J. and Rea, C. and Sweeney, R. and Granetz, R. S.},
abstractNote = {One of the most pressing challenges facing the fusion community is adequately mitigating or, even better, avoiding disruptions of tokamak plasmas. However, before this can be done, disruptions must first be predicted with sufficient warning time to actuate a response. The established field of survival analysis provides a convenient statistical framework for time-to-event (i.e. time-to-disruption) studies. This paper demonstrates the integration of an existing disruption prediction machine learning algorithm with the Kaplan-Meier estimator of survival probability. Specifically discussed are the implied warning times from binary classification of disruption databases and the interpretation of output signals from Random Forest algorithms trained and tested on these databases. This survival analysis approach is applied to both smooth and noisy test data to highlight important features of the survival and hazard functions. In addition, this method is applied to three Alcator C-Mod plasma discharges and compared to a threshold-based scheme for triggering alarms. In one case, both techniques successfully predict the disruption; although, in another, neither warns of the impending disruption with enough time to mitigate. For the final discharge, the survival analysis approach could avoid the false alarm triggered by the threshold method. Limitations of this analysis and opportunities for future work are also presented.},
doi = {10.7910/DVN/GW6HWH},
journal = {},
number = ,
volume = ,
place = {United States},
year = {2019},
month = {11}
}

Works referencing / citing this record:

An application of survival analysis to disruption prediction via Random Forests
journal, August 2019